diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index 6d86640..f660b34 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -18,9 +18,9 @@ Steps to reproduce the behavior:
 4. See error
 
 **Operating environment(运行环境):**
- - python version [e.g. 3.4, 3.6]
- - tensorflow version [e.g. 1.4.0, 1.12.0]
- - deepmatch version [e.g. 0.1.1,]
+ - python version [e.g. 3.6, 3.7]
+ - tensorflow version [e.g. 1.4.0, 1.14.0, 2.3.0]
+ - deepmatch version [e.g. 0.2.0,]
 
 **Additional context**
 Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md
index 53e8451..f6d65c0 100644
--- a/.github/ISSUE_TEMPLATE/question.md
+++ b/.github/ISSUE_TEMPLATE/question.md
@@ -17,4 +17,4 @@ Add any other context about the problem here.
 **Operating environment(运行环境):**
  - python version [e.g. 3.6]
  - tensorflow version [e.g. 1.4.0,]
- - deepmatch version [e.g. 0.1.1,]
+ - deepmatch version [e.g. 0.2.0,]
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ee1b5bc..275b40d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -18,7 +18,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.5,3.6,3.7]
-        tf-version: [1.4.0,1.14.0,2.1.0,2.2.0]
+        tf-version: [1.4.0,1.14.0,2.1.0,2.2.0,2.3.0]
 
         exclude:
           - python-version: 3.7
diff --git a/README.md b/README.md
index 74a346b..fc6d721 100644
--- a/README.md
+++ b/README.md
@@ -67,4 +67,4 @@ Please follow our wechat to join group:
 - 公众号：**浅梦的学习笔记**  
 - wechat ID: **deepctrbot**
 
-  ![wechat](./docs/pics/weichennote.png)
+  ![wechat](./docs/pics/code.png)
diff --git a/deepmatch/__init__.py b/deepmatch/__init__.py
index b1af8c4..0fa3e17 100644
--- a/deepmatch/__init__.py
+++ b/deepmatch/__init__.py
@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.1.3'
+__version__ = '0.2.0'
 check_version(__version__)
diff --git a/deepmatch/inputs.py b/deepmatch/inputs.py
index b37357a..fbb2d79 100644
--- a/deepmatch/inputs.py
+++ b/deepmatch/inputs.py
@@ -1,14 +1,17 @@
 from itertools import chain
-from deepctr.inputs import SparseFeat,VarLenSparseFeat,create_embedding_matrix,embedding_lookup,get_dense_input,varlen_embedding_lookup,get_varlen_pooling_list,mergeDict
 
-def input_from_feature_columns(features, feature_columns, l2_reg, init_std, seed, prefix='', seq_mask_zero=True,
-                               support_dense=True, support_group=False,embedding_matrix_dict=None):
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat, create_embedding_matrix, embedding_lookup, \
+    get_dense_input, varlen_embedding_lookup, get_varlen_pooling_list, mergeDict
+
+
+def input_from_feature_columns(features, feature_columns, l2_reg, seed, prefix='', seq_mask_zero=True,
+                               support_dense=True, support_group=False, embedding_matrix_dict=None):
     sparse_feature_columns = list(
         filter(lambda x: isinstance(x, SparseFeat), feature_columns)) if feature_columns else []
     varlen_sparse_feature_columns = list(
         filter(lambda x: isinstance(x, VarLenSparseFeat), feature_columns)) if feature_columns else []
-    if embedding_matrix_dict is  None:
-        embedding_matrix_dict = create_embedding_matrix(feature_columns, l2_reg, init_std, seed, prefix=prefix,
+    if embedding_matrix_dict is None:
+        embedding_matrix_dict = create_embedding_matrix(feature_columns, l2_reg, seed, prefix=prefix,
                                                         seq_mask_zero=seq_mask_zero)
 
     group_sparse_embedding_dict = embedding_lookup(embedding_matrix_dict, features, sparse_feature_columns)
@@ -22,4 +25,4 @@ def input_from_feature_columns(features, feature_columns, l2_reg, init_std, seed
     group_embedding_dict = mergeDict(group_sparse_embedding_dict, group_varlen_sparse_embedding_dict)
     if not support_group:
         group_embedding_dict = list(chain.from_iterable(group_embedding_dict.values()))
-    return group_embedding_dict, dense_value_list
\ No newline at end of file
+    return group_embedding_dict, dense_value_list
diff --git a/deepmatch/layers/__init__.py b/deepmatch/layers/__init__.py
index bd94afe..854c596 100644
--- a/deepmatch/layers/__init__.py
+++ b/deepmatch/layers/__init__.py
@@ -1,28 +1,29 @@
 from deepctr.layers import custom_objects
 from deepctr.layers.utils import reduce_sum
 
-from .core import PoolingLayer, Similarity, LabelAwareAttention, CapsuleLayer,SampledSoftmaxLayer,EmbeddingIndex
-from ..utils import sampledsoftmaxloss
-from .interaction import DotAttention, ConcatAttention, SoftmaxWeightedSum, AttentionSequencePoolingLayer, SelfAttention,\
+from .core import PoolingLayer, Similarity, LabelAwareAttention, CapsuleLayer, SampledSoftmaxLayer, EmbeddingIndex
+from .interaction import DotAttention, ConcatAttention, SoftmaxWeightedSum, AttentionSequencePoolingLayer, \
+    SelfAttention, \
     SelfMultiHeadAttention, UserAttention
 from .sequence import DynamicMultiRNN
+from ..utils import sampledsoftmaxloss
 
 _custom_objects = {'PoolingLayer': PoolingLayer,
                    'Similarity': Similarity,
                    'LabelAwareAttention': LabelAwareAttention,
                    'CapsuleLayer': CapsuleLayer,
-                   'reduce_sum':reduce_sum,
-                   'SampledSoftmaxLayer':SampledSoftmaxLayer,
-                   'sampledsoftmaxloss':sampledsoftmaxloss,
-                   'EmbeddingIndex':EmbeddingIndex,
-                   'DotAttention':DotAttention,
-                   'ConcatAttention':ConcatAttention,
-                   'SoftmaxWeightedSum':SoftmaxWeightedSum,
-                   'AttentionSequencePoolingLayer':AttentionSequencePoolingLayer,
-                   'SelfAttention':SelfAttention,
-                   'SelfMultiHeadAttention':SelfMultiHeadAttention,
-                   'UserAttention':UserAttention,
-                    'DynamicMultiRNN':DynamicMultiRNN
+                   'reduce_sum': reduce_sum,
+                   'SampledSoftmaxLayer': SampledSoftmaxLayer,
+                   'sampledsoftmaxloss': sampledsoftmaxloss,
+                   'EmbeddingIndex': EmbeddingIndex,
+                   'DotAttention': DotAttention,
+                   'ConcatAttention': ConcatAttention,
+                   'SoftmaxWeightedSum': SoftmaxWeightedSum,
+                   'AttentionSequencePoolingLayer': AttentionSequencePoolingLayer,
+                   'SelfAttention': SelfAttention,
+                   'SelfMultiHeadAttention': SelfMultiHeadAttention,
+                   'UserAttention': UserAttention,
+                   'DynamicMultiRNN': DynamicMultiRNN
                    }
 
 custom_objects = dict(custom_objects, **_custom_objects)
diff --git a/deepmatch/layers/core.py b/deepmatch/layers/core.py
index 293b885..2bdd7c2 100644
--- a/deepmatch/layers/core.py
+++ b/deepmatch/layers/core.py
@@ -1,7 +1,9 @@
 import tensorflow as tf
+from deepctr.layers.activation import activation_layer
 from deepctr.layers.utils import reduce_max, reduce_mean, reduce_sum, concat_func, div, softmax
-from tensorflow.python.keras.initializers import RandomNormal, Zeros
+from tensorflow.python.keras.initializers import RandomNormal, Zeros, glorot_normal
 from tensorflow.python.keras.layers import Layer
+from tensorflow.python.keras.regularizers import l2
 
 
 class PoolingLayer(Layer):
@@ -216,21 +218,20 @@ def squash(inputs):
     return vec_squashed
 
 
-
-
 class EmbeddingIndex(Layer):
 
-    def __init__(self, index,**kwargs):
-        self.index =index
+    def __init__(self, index, **kwargs):
+        self.index = index
         super(EmbeddingIndex, self).__init__(**kwargs)
 
     def build(self, input_shape):
-
         super(EmbeddingIndex, self).build(
             input_shape)  # Be sure to call this somewhere!
+
     def call(self, x, **kwargs):
-       return tf.constant(self.index)
+        return tf.constant(self.index)
+
     def get_config(self, ):
         config = {'index': self.index, }
         base_config = super(EmbeddingIndex, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))
\ No newline at end of file
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/deepmatch/layers/sequence.py b/deepmatch/layers/sequence.py
index a3bffc7..10e12b9 100644
--- a/deepmatch/layers/sequence.py
+++ b/deepmatch/layers/sequence.py
@@ -24,19 +24,19 @@ def build(self, input_shape):
         if self.rnn_type == "LSTM":
             try:
                 single_cell = tf.nn.rnn_cell.BasicLSTMCell(self.num_units, forget_bias=self.forget_bias)
-            except:
+            except AttributeError:
                 single_cell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell(self.num_units, forget_bias=self.forget_bias)
         elif self.rnn_type == "GRU":
             try:
                 single_cell = tf.nn.rnn_cell.GRUCell(self.num_units, forget_bias=self.forget_bias)
-            except:
+            except AttributeError:
                 single_cell = tf.compat.v1.nn.rnn_cell.GRUCell(self.num_units, forget_bias=self.forget_bias)
         else:
             raise ValueError("Unknown unit type %s!" % self.rnn_type)
         dropout = self.dropout if tf.keras.backend.learning_phase() == 1 else 0
         try:
             single_cell = tf.nn.rnn_cell.DropoutWrapper(cell=single_cell, input_keep_prob=(1.0 - dropout))
-        except:
+        except AttributeError:
             single_cell = tf.compat.v1.nn.rnn_cell.DropoutWrapper(cell=single_cell, input_keep_prob=(1.0 - dropout))
         cell_list = []
         for i in range(self.num_layers):
@@ -44,7 +44,7 @@ def build(self, input_shape):
             if residual:
                 try:
                     single_cell_residual = tf.nn.rnn_cell.ResidualWrapper(single_cell)
-                except:
+                except AttributeError:
                     single_cell_residual = tf.compat.v1.nn.rnn_cell.ResidualWrapper(single_cell)
                 cell_list.append(single_cell_residual)
             else:
@@ -54,7 +54,7 @@ def build(self, input_shape):
         else:
             try:
                 self.final_cell = tf.nn.rnn_cell.MultiRNNCell(cell_list)
-            except:
+            except AttributeError:
                 self.final_cell = tf.compat.v1.nn.rnn_cell.MultiRNNCell(cell_list)
         super(DynamicMultiRNN, self).build(input_shape)
 
@@ -66,7 +66,7 @@ def call(self, input_list, mask=None, training=None):
                 rnn_output, hidden_state = tf.nn.dynamic_rnn(self.final_cell, inputs=rnn_input,
                                                              sequence_length=tf.squeeze(sequence_length),
                                                              dtype=tf.float32, scope=self.name)
-        except:
+        except AttributeError:
             with tf.name_scope("rnn"), tf.compat.v1.variable_scope("rnn", reuse=tf.compat.v1.AUTO_REUSE):
                 rnn_output, hidden_state = tf.compat.v1.nn.dynamic_rnn(self.final_cell, inputs=rnn_input,
                                                                        sequence_length=tf.squeeze(sequence_length),
@@ -86,6 +86,6 @@ def compute_output_shape(self, input_shape):
     def get_config(self, ):
         config = {'num_units': self.num_units, 'rnn_type': self.rnn_type, 'return_sequence': self.return_sequence,
                   'num_layers': self.num_layers,
-                  'num_residual_layers': self.num_residual_layers, 'dropout_rate': self.dropout}
+                  'num_residual_layers': self.num_residual_layers, 'dropout_rate': self.dropout, 'forget_bias':self.forget_bias}
         base_config = super(DynamicMultiRNN, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
diff --git a/deepmatch/models/dssm.py b/deepmatch/models/dssm.py
index 74ac3b1..d1bd455 100644
--- a/deepmatch/models/dssm.py
+++ b/deepmatch/models/dssm.py
@@ -5,8 +5,8 @@
 Huang P S , He X , Gao J , et al. Learning deep structured semantic models for web search using clickthrough data[C]// Acm International Conference on Conference on Information & Knowledge Management. ACM, 2013.
 """
 
-from deepctr.inputs import build_input_features, combined_dnn_input, create_embedding_matrix
-from deepctr.layers.core import PredictionLayer, DNN
+from deepctr.feature_column import build_input_features, create_embedding_matrix
+from deepctr.layers import PredictionLayer, DNN, combined_dnn_input
 from tensorflow.python.keras.models import Model
 
 from ..inputs import input_from_feature_columns
@@ -16,7 +16,7 @@
 def DSSM(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64, 32),
          item_dnn_hidden_units=(64, 32),
          dnn_activation='tanh', dnn_use_bn=False,
-         l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024, metric='cos'):
+         l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024, metric='cos'):
     """Instantiates the Deep Structured Semantic Model architecture.
 
     :param user_feature_columns: An iterable containing user's features used by  the model.
@@ -28,7 +28,6 @@ def DSSM(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64,
     :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
     :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
     :param metric: str, ``"cos"`` for  cosine  or  ``"ip"`` for inner product
     :return: A Keras model instance.
@@ -36,14 +35,14 @@ def DSSM(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64,
     """
 
     embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,
-                                                    init_std, seed,
+                                                    seed=seed,
                                                     seq_mask_zero=True)
 
     user_features = build_input_features(user_feature_columns)
     user_inputs_list = list(user_features.values())
     user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns(user_features,
                                                                                    user_feature_columns,
-                                                                                   l2_reg_embedding, init_std, seed,
+                                                                                   l2_reg_embedding, seed=seed,
                                                                                    embedding_matrix_dict=embedding_matrix_dict)
     user_dnn_input = combined_dnn_input(user_sparse_embedding_list, user_dense_value_list)
 
@@ -51,15 +50,15 @@ def DSSM(user_feature_columns, item_feature_columns, user_dnn_hidden_units=(64,
     item_inputs_list = list(item_features.values())
     item_sparse_embedding_list, item_dense_value_list = input_from_feature_columns(item_features,
                                                                                    item_feature_columns,
-                                                                                   l2_reg_embedding, init_std, seed,
+                                                                                   l2_reg_embedding, seed=seed,
                                                                                    embedding_matrix_dict=embedding_matrix_dict)
     item_dnn_input = combined_dnn_input(item_sparse_embedding_list, item_dense_value_list)
 
     user_dnn_out = DNN(user_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                       dnn_use_bn, seed, )(user_dnn_input)
+                       dnn_use_bn, seed=seed)(user_dnn_input)
 
     item_dnn_out = DNN(item_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                       dnn_use_bn, seed)(item_dnn_input)
+                       dnn_use_bn, seed=seed)(item_dnn_input)
 
     score = Similarity(type=metric)([user_dnn_out, item_dnn_out])
 
diff --git a/deepmatch/models/fm.py b/deepmatch/models/fm.py
index 3b5fa91..97bf339 100644
--- a/deepmatch/models/fm.py
+++ b/deepmatch/models/fm.py
@@ -1,4 +1,4 @@
-from deepctr.inputs import build_input_features
+from deepctr.feature_column import build_input_features
 from deepctr.layers.core import PredictionLayer
 from deepctr.layers.utils import concat_func, reduce_sum
 from tensorflow.python.keras.layers import Lambda
@@ -8,13 +8,12 @@
 from ..layers.core import Similarity
 
 
-def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, init_std=0.0001, seed=1024, metric='cos'):
+def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, seed=1024, metric='cos'):
     """Instantiates the FM architecture.
 
     :param user_feature_columns: An iterable containing user's features used by  the model.
     :param item_feature_columns: An iterable containing item's features used by  the model.
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
     :param metric: str, ``"cos"`` for  cosine  or  ``"ip"`` for inner product
     :return: A Keras model instance.
@@ -22,14 +21,14 @@ def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, init_s
     """
 
     embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,
-                                                    init_std, seed,
+                                                    seed=seed,
                                                     seq_mask_zero=True)
 
     user_features = build_input_features(user_feature_columns)
     user_inputs_list = list(user_features.values())
     user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns(user_features,
                                                                                    user_feature_columns,
-                                                                                   l2_reg_embedding, init_std, seed,
+                                                                                   l2_reg_embedding, seed=seed,
                                                                                    support_dense=False,
                                                                                    embedding_matrix_dict=embedding_matrix_dict)
 
@@ -37,7 +36,7 @@ def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, init_s
     item_inputs_list = list(item_features.values())
     item_sparse_embedding_list, item_dense_value_list = input_from_feature_columns(item_features,
                                                                                    item_feature_columns,
-                                                                                   l2_reg_embedding, init_std, seed,
+                                                                                   l2_reg_embedding, seed=seed,
                                                                                    support_dense=False,
                                                                                    embedding_matrix_dict=embedding_matrix_dict)
 
diff --git a/deepmatch/models/mind.py b/deepmatch/models/mind.py
index ddd1c2c..3053dd1 100755
--- a/deepmatch/models/mind.py
+++ b/deepmatch/models/mind.py
@@ -7,11 +7,10 @@
 """
 
 import tensorflow as tf
-from deepctr.inputs import SparseFeat, VarLenSparseFeat, DenseFeat, \
-    embedding_lookup, varlen_embedding_lookup, get_varlen_pooling_list, get_dense_input, build_input_features, \
-    combined_dnn_input
-from deepctr.layers.core import DNN
-from deepctr.layers.utils import NoMask
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, \
+    embedding_lookup, varlen_embedding_lookup, get_varlen_pooling_list, get_dense_input, build_input_features
+from deepctr.layers import DNN
+from deepctr.layers.utils import NoMask, combined_dnn_input
 from tensorflow.python.keras.layers import Concatenate
 from tensorflow.python.keras.models import Model
 
@@ -30,8 +29,7 @@ def tile_user_otherfeat(user_other_feature, k_max):
 
 def MIND(user_feature_columns, item_feature_columns, num_sampled=5, k_max=2, p=1.0, dynamic_k=False,
          user_dnn_hidden_units=(64, 32), dnn_activation='relu', dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6,
-         dnn_dropout=0,
-         init_std=0.0001, seed=1024):
+         dnn_dropout=0, output_activation='linear', seed=1024):
     """Instantiates the MIND Model architecture.
 
     :param user_feature_columns: An iterable containing user's features used by  the model.
@@ -47,8 +45,8 @@ def MIND(user_feature_columns, item_feature_columns, num_sampled=5, k_max=2, p=1
     :param l2_reg_dnn:  L2 regularizer strength applied to DNN
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
     :param dnn_dropout:  float in [0,1), the probability we will drop out a given DNN coordinate.
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
+    :param output_activation: Activation function to use in output layer
     :return: A Keras model instance.
 
     """
@@ -83,8 +81,7 @@ def MIND(user_feature_columns, item_feature_columns, num_sampled=5, k_max=2, p=1
     inputs_list = list(features.values())
 
     embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,
-                                                    init_std,
-                                                    seed, prefix="")
+                                                    seed=seed, prefix="")
 
     item_features = build_input_features(item_feature_columns)
 
@@ -127,7 +124,9 @@ def MIND(user_feature_columns, item_feature_columns, num_sampled=5, k_max=2, p=1
         user_deep_input = high_capsule
 
     user_embeddings = DNN(user_dnn_hidden_units, dnn_activation, l2_reg_dnn,
-                          dnn_dropout, dnn_use_bn, seed, name="user_embedding")(user_deep_input)
+                          dnn_dropout, dnn_use_bn, output_activation=output_activation, seed=seed,
+                          name="user_embedding")(
+        user_deep_input)
     item_inputs_list = list(item_features.values())
 
     item_embedding_matrix = embedding_matrix_dict[item_feature_name]
diff --git a/deepmatch/models/ncf.py b/deepmatch/models/ncf.py
index 2de4c5f..95be769 100644
--- a/deepmatch/models/ncf.py
+++ b/deepmatch/models/ncf.py
@@ -8,8 +8,8 @@
 
 import math
 
-from deepctr.inputs import input_from_feature_columns, build_input_features, combined_dnn_input, SparseFeat
-from deepctr.layers.core import DNN
+from deepctr.feature_column import input_from_feature_columns, build_input_features, SparseFeat
+from deepctr.layers import DNN, combined_dnn_input
 from tensorflow.python.keras.layers import Lambda, Concatenate, Multiply
 from tensorflow.python.keras.models import Model
 
@@ -17,7 +17,7 @@
 def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, item_gmf_embedding_dim=20,
         user_mlp_embedding_dim=20, item_mlp_embedding_dim=20, dnn_use_bn=False,
         dnn_hidden_units=(64, 32), dnn_activation='relu', l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0,
-        init_std=0.0001, seed=1024):
+        seed=1024):
     """Instantiates the NCF Model architecture.
 
     :param user_feature_columns: A dict containing user's features and features'dim.
@@ -32,7 +32,6 @@ def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, i
     :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
     :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
     :return: A Keras model instance.
 
@@ -51,8 +50,8 @@ def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, i
     user_inputs_list = list(user_features.values())
     user_gmf_sparse_embedding_list, user_gmf_dense_value_list = input_from_feature_columns(user_features,
                                                                                            user_gmf_feature_columns,
-                                                                                           l2_reg_embedding, init_std,
-                                                                                           seed, prefix='gmf_')
+                                                                                           l2_reg_embedding, seed=seed,
+                                                                                           prefix='gmf_')
     user_gmf_input = combined_dnn_input(user_gmf_sparse_embedding_list, [])
     user_gmf_out = Lambda(lambda x: x, name="user_gmf_embedding")(user_gmf_input)
 
@@ -62,8 +61,8 @@ def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, i
     item_inputs_list = list(item_features.values())
     item_gmf_sparse_embedding_list, item_gmf_dense_value_list = input_from_feature_columns(item_features,
                                                                                            item_gmf_feature_columns,
-                                                                                           l2_reg_embedding, init_std,
-                                                                                           seed, prefix='gmf_')
+                                                                                           l2_reg_embedding, seed=seed,
+                                                                                           prefix='gmf_')
     item_gmf_input = combined_dnn_input(item_gmf_sparse_embedding_list, [])
     item_gmf_out = Lambda(lambda x: x, name="item_gmf_embedding")(item_gmf_input)
 
@@ -74,8 +73,8 @@ def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, i
                                 for feat, size in user_feature_columns.items()]
     user_mlp_sparse_embedding_list, user_mlp_dense_value_list = input_from_feature_columns(user_features,
                                                                                            user_mlp_feature_columns,
-                                                                                           l2_reg_embedding, init_std,
-                                                                                           seed, prefix='mlp_')
+                                                                                           l2_reg_embedding, seed=seed,
+                                                                                           prefix='mlp_')
     user_mlp_input = combined_dnn_input(
         user_mlp_sparse_embedding_list, user_mlp_dense_value_list)
     user_mlp_out = Lambda(lambda x: x, name="user_mlp_embedding")(user_mlp_input)
@@ -85,19 +84,19 @@ def NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20, i
 
     item_mlp_sparse_embedding_list, item_mlp_dense_value_list = input_from_feature_columns(item_features,
                                                                                            item_mlp_feature_columns,
-                                                                                           l2_reg_embedding, init_std,
-                                                                                           seed, prefix='mlp_')
+                                                                                           l2_reg_embedding, seed=seed,
+                                                                                           prefix='mlp_')
     item_mlp_input = combined_dnn_input(
         item_mlp_sparse_embedding_list, item_mlp_dense_value_list)
     item_mlp_out = Lambda(lambda x: x, name="item_mlp_embedding")(item_mlp_input)
 
     mlp_input = Concatenate(axis=1)([user_mlp_out, item_mlp_out])
     mlp_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                  dnn_use_bn, seed, name="mlp_embedding")(mlp_input)
+                  dnn_use_bn, seed = seed, name="mlp_embedding")(mlp_input)
 
     # Fusion of GMF and MLP
     neumf_input = Concatenate(axis=1)([gmf_out, mlp_out])
-    neumf_out = DNN(hidden_units=[1], activation='sigmoid')(neumf_input)
+    neumf_out = DNN(hidden_units=[1], activation='sigmoid',seed=seed)(neumf_input)
     output = Lambda(lambda x: x, name='neumf_out')(neumf_out)
 
     # output = PredictionLayer(task, False)(neumf_out)
diff --git a/deepmatch/models/sdm.py b/deepmatch/models/sdm.py
index 8119fcf..6d6ca96 100644
--- a/deepmatch/models/sdm.py
+++ b/deepmatch/models/sdm.py
@@ -8,10 +8,11 @@
 """
 
 import tensorflow as tf
-from deepctr.inputs import build_input_features, SparseFeat, DenseFeat, get_varlen_pooling_list, VarLenSparseFeat, \
+from deepctr.feature_column import build_input_features, SparseFeat, DenseFeat, get_varlen_pooling_list, \
+    VarLenSparseFeat, \
     create_embedding_matrix, embedding_lookup, varlen_embedding_lookup, concat_func
 from deepctr.layers.utils import NoMask
-from tensorflow.python.keras.layers import Dense, Input, Lambda
+from tensorflow.python.keras.layers import Dense, Lambda
 from tensorflow.python.keras.models import Model
 
 from deepmatch.utils import get_item_embedding
@@ -20,9 +21,10 @@
 from ..layers.sequence import DynamicMultiRNN
 
 
-def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sampled=5, units=64, rnn_layers=2, dropout_rate=0.2,
+def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sampled=5, units=64, rnn_layers=2,
+        dropout_rate=0.2,
         rnn_num_res=1,
-        num_head=4, l2_reg_embedding=1e-6, dnn_activation='tanh', init_std=0.0001, seed=1024):
+        num_head=4, l2_reg_embedding=1e-6, dnn_activation='tanh', seed=1024):
     """Instantiates the Sequential Deep Matching Model architecture.
 
     :param user_feature_columns: An iterable containing user's features used by  the model.
@@ -36,14 +38,13 @@ def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sa
     :param num_head: int int, the number of attention head
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
     :param dnn_activation: Activation function to use in deep net
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
     :return: A Keras model instance.
 
     """
 
     if len(item_feature_columns) > 1:
-        raise ValueError("Now MIND only support 1 item feature like item_id")
+        raise ValueError("Now SDM only support 1 item feature like item_id")
     item_feature_column = item_feature_columns[0]
     item_feature_name = item_feature_column.name
     item_vocabulary_size = item_feature_columns[0].vocabulary_size
@@ -78,8 +79,7 @@ def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sa
             sparse_varlen_feature_columns.append(fc)
 
     embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,
-                                                    init_std, seed,
-                                                    prefix="")
+                                                    seed=seed)
 
     item_features = build_input_features(item_feature_columns)
     item_inputs_list = list(item_features.values())
@@ -142,11 +142,22 @@ def SDM(user_feature_columns, item_feature_columns, history_feature_list, num_sa
         pooling_item_embedding_weight, gate_output_reshape, item_features[item_feature_name]])
     model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)
 
+    # model.user_input = user_inputs_list
+    # model.user_embedding = gate_output_reshape
+
     model.__setattr__("user_input", user_inputs_list)
     model.__setattr__("user_embedding", gate_output_reshape)
 
+    # model.item_input = item_inputs_list
+    # model.item_embedding = get_item_embedding(pooling_item_embedding_weight, item_features[item_feature_name])
+
     model.__setattr__("item_input", item_inputs_list)
     model.__setattr__("item_embedding",
                       get_item_embedding(pooling_item_embedding_weight, item_features[item_feature_name]))
 
     return model
+    # , Model(inputs=user_inputs_list, outputs=gate_output_reshape), Model(inputs=item_inputs_list,
+    #                                                                              outputs=get_item_embedding(
+    #                                                                                  pooling_item_embedding_weight,
+    #                                                                                  item_features[
+    #                                                                                      item_feature_name]))
diff --git a/deepmatch/models/youtubednn.py b/deepmatch/models/youtubednn.py
index f17087f..3b96a70 100644
--- a/deepmatch/models/youtubednn.py
+++ b/deepmatch/models/youtubednn.py
@@ -4,21 +4,21 @@
 Reference:
 Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 191-198.
 """
-from deepctr.inputs import input_from_feature_columns, build_input_features, combined_dnn_input, create_embedding_matrix
-from deepctr.layers.core import DNN
-from deepctr.layers.utils import NoMask
+from deepctr.feature_column import build_input_features
+from deepctr.layers import DNN
+from deepctr.layers.utils import NoMask, combined_dnn_input
 from tensorflow.python.keras.models import Model
 
 from deepmatch.layers import PoolingLayer
 from deepmatch.utils import get_item_embedding
-from ..inputs import input_from_feature_columns
+from ..inputs import input_from_feature_columns, create_embedding_matrix
 from ..layers.core import SampledSoftmaxLayer, EmbeddingIndex
 
 
 def YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5,
                user_dnn_hidden_units=(64, 32),
                dnn_activation='relu', dnn_use_bn=False,
-               l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, init_std=0.0001, seed=1024, ):
+               l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, output_activation='linear', seed=1024, ):
     """Instantiates the YoutubeDNN Model architecture.
 
     :param user_feature_columns: An iterable containing user's features used by  the model.
@@ -30,8 +30,8 @@ def YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5,
     :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
     :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
     :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
-    :param init_std: float,to use as the initialize std of embedding vector
     :param seed: integer ,to use as random seed.
+    :param output_activation: Activation function to use in output layer
     :return: A Keras model instance.
 
     """
@@ -42,20 +42,19 @@ def YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5,
     item_vocabulary_size = item_feature_columns[0].vocabulary_size
 
     embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,
-                                                    init_std, seed, prefix="")
+                                                    seed=seed)
 
     user_features = build_input_features(user_feature_columns)
     user_inputs_list = list(user_features.values())
-    user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns(user_features,
-                                                                                   user_feature_columns,
-                                                                                   l2_reg_embedding, init_std, seed,
+    user_sparse_embedding_list, user_dense_value_list = input_from_feature_columns(user_features, user_feature_columns,
+                                                                                   l2_reg_embedding, seed=seed,
                                                                                    embedding_matrix_dict=embedding_matrix_dict)
     user_dnn_input = combined_dnn_input(user_sparse_embedding_list, user_dense_value_list)
 
     item_features = build_input_features(item_feature_columns)
     item_inputs_list = list(item_features.values())
     user_dnn_out = DNN(user_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
-                       dnn_use_bn, seed, )(user_dnn_input)
+                       dnn_use_bn, output_activation=output_activation, seed=seed)(user_dnn_input)
 
     item_index = EmbeddingIndex(list(range(item_vocabulary_size)))(item_features[item_feature_name])
 
diff --git a/docs/pics/code.png b/docs/pics/code.png
new file mode 100644
index 0000000..aa53cbf
Binary files /dev/null and b/docs/pics/code.png differ
diff --git a/docs/pics/weichennote.png b/docs/pics/weichennote.png
index fec7b11..0b60a2f 100644
Binary files a/docs/pics/weichennote.png and b/docs/pics/weichennote.png differ
diff --git a/docs/source/Examples.md b/docs/source/Examples.md
index dc2a7f4..2e652e2 100644
--- a/docs/source/Examples.md
+++ b/docs/source/Examples.md
@@ -20,7 +20,7 @@ This example shows how to use ``YoutubeDNN`` to solve a matching task. You can g
 
 ```python
 import pandas as pd
-from deepctr.inputs import SparseFeat, VarLenSparseFeat
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat
 from preprocess import gen_data_set, gen_model_input
 from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras import backend as K
@@ -292,7 +292,7 @@ This example shows how to use ``DSSM`` to solve a matching task. You can get the
 
 ```python
 import pandas as pd
-from deepctr.inputs import SparseFeat, VarLenSparseFeat
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat
 from preprocess import gen_data_set, gen_model_input
 from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras.models import Model
diff --git a/docs/source/Features.md b/docs/source/Features.md
index d7acce9..ed3831a 100644
--- a/docs/source/Features.md
+++ b/docs/source/Features.md
@@ -2,15 +2,17 @@
 
 ## Feature Columns
 ### SparseFeat
-``SparseFeat`` is a namedtuple with signature ``SparseFeat(name, vocabulary_size, embedding_dim, use_hash, dtype,embedding_name, group_name)``
+``SparseFeat`` is a namedtuple with signature ``SparseFeat(name, vocabulary_size, embedding_dim, use_hash, dtype, embeddings_initializer, embedding_name, group_name, trainable)``
 
 - name : feature name
 - vocabulary_size : number of unique feature values for sprase feature or hashing space when `use_hash=True`
 - embedding_dim : embedding dimension
 - use_hash : defualt `False`.If `True` the input will be hashed to space of size `vocabulary_size`.
-- dtype : default `float32`.dtype of input tensor.
+- dtype : default `int32`.dtype of input tensor.
+- embeddings_initializer : initializer for the `embeddings` matrix.
 - embedding_name : default `None`. If None, the embedding_name will be same as `name`.
 - group_name : feature group of this feature.
+- trainable: default `True`.Whether or not the embedding is trainable.
 
 ### DenseFeat
 ``DenseFeat`` is a namedtuple with signature ``DenseFeat(name, dimension, dtype)``
@@ -30,6 +32,7 @@
 - weight_name : default `None`. If not None, the sequence feature will be multiplyed by the feature whose name is `weight_name`.
 - weight_norm : default `True`. Whether normalize the weight score or not.
 
+
 ## Models
 
 
diff --git a/docs/source/History.md b/docs/source/History.md
index 1645b66..d0c9f61 100644
--- a/docs/source/History.md
+++ b/docs/source/History.md
@@ -1,4 +1,5 @@
 # History
+- 10/12/2020 : [v0.2.0](https://github.com/shenweichen/DeepMatch/releases/tag/v0.2.0) released.Support different initializers for different embedding weights and loading pretrained embeddings. 
 - 05/17/2020 : [v0.1.3](https://github.com/shenweichen/DeepMatch/releases/tag/v0.1.3) released.Add `SDM` model . 
 - 04/10/2020 : [v0.1.2](https://github.com/shenweichen/DeepMatch/releases/tag/v0.1.2) released.Support [saving and loading model](./FAQ.html#save-or-load-weights-models). 
 - 04/06/2020 : DeepMatch first version  is released on [PyPi](https://pypi.org/project/deepmatch/)
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0a25c61..4e882a8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '0.1.3'
+release = '0.2.0'
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index a774fe7..f5b25cc 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -18,18 +18,18 @@ You can read the latest code at https://github.com/shenweichen/DeepMatch
 News
 -----
 
+10/12/2020 : Support different initializers for different embedding weights and loading pretrained embeddings. `Changelog <https://github.com/shenweichen/DeepMatch/releases/tag/v0.2.0>`_
+
 05/17/2020 : Add ``SDM`` model. `Changelog <https://github.com/shenweichen/DeepMatch/releases/tag/v0.1.3>`_
 
 04/10/2020 : Support `saving and loading model <./FAQ.html#save-or-load-weights-models>`_  . `Changelog <https://github.com/shenweichen/DeepMatch/releases/tag/v0.1.2>`_
 
-04/06/2020 : DeepMatch first version .
-
 DisscussionGroup
 -----------------------
 
 公众号：**浅梦的学习笔记**  wechat ID: **deepctrbot**
 
-.. image:: ../pics/weichennote.png
+.. image:: ../pics/code.png
 
 .. toctree::
    :maxdepth: 2
diff --git a/examples/colab_MovieLen1M_YoutubeDNN.ipynb b/examples/colab_MovieLen1M_YoutubeDNN.ipynb
index 6f78370..a5bd90e 100644
--- a/examples/colab_MovieLen1M_YoutubeDNN.ipynb
+++ b/examples/colab_MovieLen1M_YoutubeDNN.ipynb
@@ -159,7 +159,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "from deepctr.inputs import SparseFeat, VarLenSparseFeat\n",
+    "from deepctr.feature_column import SparseFeat, VarLenSparseFeat\n",
     "from preprocess import gen_data_set, gen_model_input\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "from tensorflow.python.keras import backend as K\n",
@@ -386,7 +386,7 @@
     "    tf.compat.v1.disable_eager_execution()\n",
     "\n",
     "model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=100, user_dnn_hidden_units=(128,64, embedding_dim))\n",
-    "# model = MIND(user_feature_columns,item_feature_columns,dynamic_k=False,p=1,k_max=2,num_sampled=100,user_dnn_hidden_units=(128,64, embedding_dim),init_std=0.001)\n",
+    "# model = MIND(user_feature_columns,item_feature_columns,dynamic_k=False,p=1,k_max=2,num_sampled=100,user_dnn_hidden_units=(128,64, embedding_dim))\n",
     "\n",
     "model.compile(optimizer=\"adam\", loss=sampledsoftmaxloss)  # \"binary_crossentropy\")\n",
     "\n",
diff --git a/examples/preprocess.py b/examples/preprocess.py
index 876bc12..4803749 100644
--- a/examples/preprocess.py
+++ b/examples/preprocess.py
@@ -104,8 +104,8 @@ def gen_model_input_sdm(train_set, user_profile, seq_short_len, seq_prefer_len):
                                         value=0)
 
     train_model_input = {"user_id": train_uid, "movie_id": train_iid, "short_movie_id": train_short_item_pad,
-        "prefer_movie_id": train_prefer_item_pad, "prefer_sess_length": train_short_len, "short_sess_length":
-        train_prefer_len, 'short_genres': train_short_genres_pad, 'prefer_genres': train_prefer_genres_pad}
+        "prefer_movie_id": train_prefer_item_pad, "prefer_sess_length": train_prefer_len, "short_sess_length":
+        train_short_len, 'short_genres': train_short_genres_pad, 'prefer_genres': train_prefer_genres_pad}
 
     for key in ["gender", "age", "occupation", "zip"]:
         train_model_input[key] = user_profile.loc[train_model_input['user_id']][key].values
diff --git a/examples/run_dssm_negsampling.py b/examples/run_dssm_negsampling.py
index e40a0c1..2ed6d83 100644
--- a/examples/run_dssm_negsampling.py
+++ b/examples/run_dssm_negsampling.py
@@ -1,5 +1,5 @@
 import pandas as pd
-from deepctr.inputs import SparseFeat, VarLenSparseFeat
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat
 from preprocess import gen_data_set, gen_model_input
 from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras.models import Model
diff --git a/examples/run_ncf.py b/examples/run_ncf.py
new file mode 100644
index 0000000..29d0358
--- /dev/null
+++ b/examples/run_ncf.py
@@ -0,0 +1,56 @@
+import pandas as pd
+from preprocess import gen_data_set, gen_model_input
+from sklearn.preprocessing import LabelEncoder
+
+from deepmatch.models import NCF
+
+if __name__ == "__main__":
+    data = pd.read_csvdata = pd.read_csv("./movielens_sample.txt")
+    sparse_features = ["movie_id", "user_id",
+                       "gender", "age", "occupation", "zip", ]
+    SEQ_LEN = 50
+    negsample = 3
+
+    # 1.Label Encoding for sparse features,and process sequence features with `gen_date_set` and `gen_model_input`
+
+    features = ['user_id', 'movie_id', 'gender', 'age', 'occupation', 'zip']
+    feature_max_idx = {}
+    for feature in features:
+        lbe = LabelEncoder()
+        data[feature] = lbe.fit_transform(data[feature]) + 1
+        feature_max_idx[feature] = data[feature].max() + 1
+
+    user_profile = data[["user_id", "gender", "age", "occupation", "zip"]].drop_duplicates('user_id')
+
+    item_profile = data[["movie_id"]].drop_duplicates('movie_id')
+
+    user_profile.set_index("user_id", inplace=True)
+
+    user_item_list = data.groupby("user_id")['movie_id'].apply(list)
+
+    train_set, test_set = gen_data_set(data, negsample)
+
+    train_model_input, train_label = gen_model_input(train_set, user_profile, SEQ_LEN)
+    test_model_input, test_label = gen_model_input(test_set, user_profile, SEQ_LEN)
+
+    # 2.count #unique features for each sparse field and generate feature config for sequence feature
+
+    user_feature_columns = {"user_id": feature_max_idx['user_id'], 'gender': feature_max_idx['gender'],
+                            "age": feature_max_idx['age'],
+                            "occupation": feature_max_idx["occupation"], "zip": feature_max_idx["zip"]}
+
+    item_feature_columns = {"movie_id": feature_max_idx['movie_id']}
+
+    # 3.Define Model,train,predict and evaluate
+    model = NCF(user_feature_columns, item_feature_columns, user_gmf_embedding_dim=20,
+                item_gmf_embedding_dim=20, user_mlp_embedding_dim=32, item_mlp_embedding_dim=32,
+                dnn_hidden_units=[128, 64, 32], )
+    model.summary()
+    model.compile("adam", "binary_crossentropy",
+                  metrics=['binary_crossentropy'], )
+
+    history = model.fit(train_model_input, train_label,
+                        batch_size=64, epochs=20, verbose=2, validation_split=0.2, )
+    pred_ans = model.predict(test_model_input, batch_size=64)
+    # print("test LogLoss", round(log_loss(test_label, pred_ans), 4))
+    # print("test AUC", round(roc_auc_score(test_label, pred_ans), 4))
diff --git a/examples/run_sdm.py b/examples/run_sdm.py
index e409414..fdf1def 100644
--- a/examples/run_sdm.py
+++ b/examples/run_sdm.py
@@ -1,5 +1,5 @@
 import pandas as pd
-from deepctr.inputs import SparseFeat, VarLenSparseFeat
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat
 from preprocess import gen_data_set_sdm, gen_model_input_sdm
 from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras import backend as K
@@ -74,16 +74,13 @@
     model = SDM(user_feature_columns, item_feature_columns, history_feature_list=['movie_id', 'genres'],
                 units=embedding_dim, num_sampled=100, )
 
-    optimizer = optimizers.Adam(lr=0.001, clipnorm=5.0)
-
-    model.compile(optimizer=optimizer, loss=sampledsoftmaxloss)  # "binary_crossentropy")
+    model.compile(optimizer='adam', loss=sampledsoftmaxloss)  # "binary_crossentropy")
 
     history = model.fit(train_model_input, train_label,  # train_label,
                         batch_size=512, epochs=1, verbose=1, validation_split=0.0, )
-    # model.save_weights('SDM_weights.h5')
 
     K.set_learning_phase(False)
-    # 4. Generate user features for testing and full item features for retrieval
+    # 3.Define Model,train,predict and evaluate
     test_user_model_input = test_model_input
     all_item_model_input = {"movie_id": item_profile['movie_id'].values, }
 
diff --git a/examples/run_youtubednn.py b/examples/run_youtubednn.py
index aeb36c0..d98a472 100644
--- a/examples/run_youtubednn.py
+++ b/examples/run_youtubednn.py
@@ -1,5 +1,5 @@
 import pandas as pd
-from deepctr.inputs import SparseFeat, VarLenSparseFeat
+from deepctr.feature_column import SparseFeat, VarLenSparseFeat
 from preprocess import gen_data_set, gen_model_input
 from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras import backend as K
@@ -60,7 +60,7 @@
        tf.compat.v1.disable_eager_execution()
 
     model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=5, user_dnn_hidden_units=(64, embedding_dim))
-    # model = MIND(user_feature_columns,item_feature_columns,dynamic_k=False,p=1,k_max=2,num_sampled=5,user_dnn_hidden_units=(64, embedding_dim),init_std=0.001)
+    #model = MIND(user_feature_columns,item_feature_columns,dynamic_k=False,p=1,k_max=2,num_sampled=5,user_dnn_hidden_units=(64, embedding_dim))
 
     model.compile(optimizer="adam", loss=sampledsoftmaxloss)  # "binary_crossentropy")
 
diff --git a/setup.py b/setup.py
index fa915ee..8ab2c5c 100644
--- a/setup.py
+++ b/setup.py
@@ -4,12 +4,12 @@
     long_description = fh.read()
 
 REQUIRED_PACKAGES = [
-    'h5py','requests',"deepctr==0.7.5"
+    'h5py', 'requests', "deepctr==0.8.2"
 ]
 
 setuptools.setup(
     name="deepmatch",
-    version="0.1.3",
+    version="0.2.0",
     author="Weichen Shen",
     author_email="wcshen1994@163.com",
     description="Deep matching model library for recommendations, advertising. It's easy to train models and to **export representation vectors** for user and item which can be used for **ANN search**.",
@@ -45,6 +45,6 @@
         'Topic :: Software Development :: Libraries :: Python Modules',
     ),
     license="Apache-2.0",
-    keywords=['match', 'matching','recommendation'
-              'deep learning', 'tensorflow', 'tensor', 'keras'],
+    keywords=['match', 'matching', 'recommendation'
+                                   'deep learning', 'tensorflow', 'tensor', 'keras'],
 )
diff --git a/tests/models/DSSM_test.py b/tests/models/DSSM_test.py
index 90c5e24..c4210e8 100644
--- a/tests/models/DSSM_test.py
+++ b/tests/models/DSSM_test.py
@@ -2,9 +2,6 @@
 from ..utils import check_model, get_xy_fd
 
 
-# @pytest.mark.xfail(reason="There is a bug when save model use Dice")
-# @pytest.mark.skip(reason="misunderstood the API")
-
 
 def test_DSSM():
     model_name = "DSSM"
diff --git a/tests/models/FM_test.py b/tests/models/FM_test.py
index 0ae1222..baa224e 100644
--- a/tests/models/FM_test.py
+++ b/tests/models/FM_test.py
@@ -2,10 +2,6 @@
 from ..utils import check_model, get_xy_fd
 
 
-# @pytest.mark.xfail(reason="There is a bug when save model use Dice")
-# @pytest.mark.skip(reason="misunderstood the API")
-
-
 def test_FM():
     model_name = "FM"
 
diff --git a/tests/models/MIND_test.py b/tests/models/MIND_test.py
index 9451630..0b9509b 100644
--- a/tests/models/MIND_test.py
+++ b/tests/models/MIND_test.py
@@ -1,12 +1,9 @@
-from deepmatch.models import MIND
-from deepmatch.utils import sampledsoftmaxloss
-from tensorflow.python.keras import backend as K
-from ..utils import check_model,get_xy_fd
 import tensorflow as tf
+from tensorflow.python.keras import backend as K
 
-
-#@pytest.mark.xfail(reason="There is a bug when save model use Dice")
-#@pytest.mark.skip(reason="misunderstood the API")
+from deepmatch.models import MIND
+from deepmatch.utils import sampledsoftmaxloss
+from ..utils import check_model, get_xy_fd
 
 
 def test_MIND():
@@ -16,12 +13,12 @@ def test_MIND():
     K.set_learning_phase(True)
 
     if tf.__version__ >= '2.0.0':
-       tf.compat.v1.disable_eager_execution()
+        tf.compat.v1.disable_eager_execution()
 
     model = MIND(user_feature_columns, item_feature_columns, num_sampled=2, user_dnn_hidden_units=(16, 4))
 
     model.compile('adam', sampledsoftmaxloss)
-    check_model(model,model_name,x,y)
+    check_model(model, model_name, x, y)
 
 
 if __name__ == "__main__":
diff --git a/tests/models/NCF_test.py b/tests/models/NCF_test.py
new file mode 100644
index 0000000..da609c0
--- /dev/null
+++ b/tests/models/NCF_test.py
@@ -0,0 +1,16 @@
+from deepmatch.models import NCF
+from ..utils import  get_xy_fd_ncf
+
+
+def test_NCF():
+    model_name = "NCF"
+
+    x, y, user_feature_columns, item_feature_columns = get_xy_fd_ncf(False)
+    model = NCF(user_feature_columns, item_feature_columns, )
+
+    model.compile('adam', "binary_crossentropy")
+    model.fit(x, y, batch_size=10, epochs=2, validation_split=0.5)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/tests/models/SDM_test.py b/tests/models/SDM_test.py
index b4caf9b..60e3e5a 100644
--- a/tests/models/SDM_test.py
+++ b/tests/models/SDM_test.py
@@ -1,12 +1,10 @@
-from deepmatch.models import SDM
-from deepmatch.utils import sampledsoftmaxloss
-from tensorflow.python.keras import backend as K
-from tests.utils import check_model, get_xy_fd_sdm
 import tensorflow as tf
+from tensorflow.python.keras import backend as K
 
+from deepmatch.models import SDM
+from deepmatch.utils import sampledsoftmaxloss
+from ..utils import check_model, get_xy_fd_sdm
 
-#@pytest.mark.xfail(reason="There is a bug when save model use Dice")
-#@pytest.mark.skip(reason="misunderstood the API")
 
 
 def test_SDM():
@@ -16,14 +14,14 @@ def test_SDM():
     K.set_learning_phase(True)
 
     if tf.__version__ >= '2.0.0':
-       tf.compat.v1.disable_eager_execution()
+        tf.compat.v1.disable_eager_execution()
 
     model = SDM(user_feature_columns, item_feature_columns, history_feature_list, units=8)
-    #model.summary()
+    # model.summary()
 
     model.compile('adam', sampledsoftmaxloss)
     check_model(model, model_name, x, y)
 
 
 if __name__ == "__main__":
-    pass
\ No newline at end of file
+    pass
diff --git a/tests/models/YoutubeDNN_test.py b/tests/models/YoutubeDNN_test.py
index ea7560c..46afb03 100644
--- a/tests/models/YoutubeDNN_test.py
+++ b/tests/models/YoutubeDNN_test.py
@@ -1,13 +1,9 @@
-from deepmatch.models import YoutubeDNN
-from deepmatch.utils import sampledsoftmaxloss
-from tensorflow.python.keras import backend as K
 import tensorflow as tf
+from tensorflow.python.keras import backend as K
 
-from ..utils import check_model,get_xy_fd
-
-
-#@pytest.mark.xfail(reason="There is a bug when save model use Dice")
-#@pytest.mark.skip(reason="misunderstood the API")
+from deepmatch.models import YoutubeDNN
+from deepmatch.utils import sampledsoftmaxloss
+from ..utils import check_model, get_xy_fd
 
 
 def test_YoutubeDNN():
@@ -17,12 +13,12 @@ def test_YoutubeDNN():
     K.set_learning_phase(True)
 
     if tf.__version__ >= '2.0.0':
-       tf.compat.v1.disable_eager_execution()
+        tf.compat.v1.disable_eager_execution()
 
     model = YoutubeDNN(user_feature_columns, item_feature_columns, num_sampled=2, user_dnn_hidden_units=(16, 4))
     model.compile('adam', sampledsoftmaxloss)
 
-    check_model(model,model_name,x,y,check_model_io=True)
+    check_model(model, model_name, x, y, check_model_io=True)
 
 
 if __name__ == "__main__":
diff --git a/tests/utils.py b/tests/utils.py
index 08be5fb..b58e9db 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -6,12 +6,12 @@
 
 import numpy as np
 import tensorflow as tf
+from deepctr.feature_column import SparseFeat, DenseFeat, VarLenSparseFeat, DEFAULT_GROUP_NAME
 from numpy.testing import assert_allclose
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.layers import Input, Masking
 from tensorflow.python.keras.models import Model, load_model, save_model
 
-from deepctr.inputs import SparseFeat, DenseFeat, VarLenSparseFeat,DEFAULT_GROUP_NAME
 from deepmatch.layers import custom_objects
 
 SAMPLE_SIZE = 8
@@ -44,12 +44,13 @@ def get_test_data(sample_size=1000, embedding_size=4, sparse_feature_num=1, dens
 
     for i in range(sparse_feature_num):
         if use_group:
-            group_name = str(i%3)
+            group_name = str(i % 3)
         else:
             group_name = DEFAULT_GROUP_NAME
         dim = np.random.randint(1, 10)
         feature_columns.append(
-            SparseFeat(prefix + 'sparse_feature_' + str(i), dim, embedding_size, use_hash=hash_flag, dtype=tf.int32,group_name=group_name))
+            SparseFeat(prefix + 'sparse_feature_' + str(i), dim, embedding_size, use_hash=hash_flag, dtype=tf.int32,
+                       group_name=group_name))
 
     for i in range(dense_feature_num):
         feature_columns.append(DenseFeat(prefix + 'dense_feature_' + str(i), 1, dtype=tf.float32))
@@ -340,11 +341,8 @@ def check_model(model, model_name, x, y, check_model_io=True):
 
     model.fit(x, y, batch_size=10, epochs=2, validation_split=0.5)
 
-
-
     print(model_name + " test train valid pass!")
 
-
     user_embedding_model = Model(inputs=model.user_input, outputs=model.user_embedding)
     item_embedding_model = Model(inputs=model.item_input, outputs=model.item_embedding)
 
@@ -366,47 +364,79 @@ def check_model(model, model_name, x, y, check_model_io=True):
         print(model_name + " test save load model pass!")
 
     print(model_name + " test pass!")
+    # print(1)
+    #
+    # save_model(item_embedding_model, model_name + '.user.h5')
+    # print(2)
+    #
+    # item_embedding_model = load_model(model_name + '.user.h5', custom_objects)
+    # print(3)
+    #
+    # item_embs = item_embedding_model.predict(x, batch_size=2 ** 12)
+    # print(item_embs)
+    # print("go")
 
 
 def get_xy_fd(hash_flag=False):
+    user_feature_columns = [SparseFeat('user', 3), SparseFeat(
+        'gender', 2), VarLenSparseFeat(
+        SparseFeat('hist_item', vocabulary_size=3 + 1, embedding_dim=4, embedding_name='item'), maxlen=4,
+        length_name="hist_len")]
+    item_feature_columns = [SparseFeat('item', 3 + 1, embedding_dim=4, )]
+
+    uid = np.array([0, 1, 2, 1])
+    ugender = np.array([0, 1, 0, 1])
+    iid = np.array([1, 2, 3, 1])  # 0 is mask value
+
+    hist_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0], [3, 0, 0, 0]])
+    hist_len = np.array([3, 3, 2, 1])
 
+    feature_dict = {'user': uid, 'gender': ugender, 'item': iid,
+                    'hist_item': hist_iid, "hist_len": hist_len}
+
+    # feature_names = get_feature_names(feature_columns)
+    x = feature_dict
+    y = np.array([1, 1, 1, 1])
+    return x, y, user_feature_columns, item_feature_columns
 
-    user_feature_columns = [SparseFeat('user',3),SparseFeat(
-        'gender', 2),VarLenSparseFeat(SparseFeat('hist_item', vocabulary_size=3 + 1,embedding_dim=4,embedding_name='item'), maxlen=4,length_name="hist_len") ]
-    item_feature_columns = [SparseFeat('item', 3 + 1,embedding_dim=4,)]
 
+def get_xy_fd_ncf(hash_flag=False):
+    user_feature_columns = {"user": 3, "gender": 2, }
+    item_feature_columns = {"item": 4}
 
-    uid = np.array([0, 1, 2,1])
-    ugender = np.array([0, 1, 0,1])
-    iid = np.array([1, 2, 3,1])  # 0 is mask value
+    uid = np.array([0, 1, 2, 1])
+    ugender = np.array([0, 1, 0, 1])
+    iid = np.array([1, 2, 3, 1])  # 0 is mask value
 
-    hist_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0],[3, 0, 0, 0]])
-    hist_len = np.array([3,3,2,1])
+    hist_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [1, 2, 0, 0], [3, 0, 0, 0]])
+    hist_len = np.array([3, 3, 2, 1])
 
     feature_dict = {'user': uid, 'gender': ugender, 'item': iid,
-                    'hist_item': hist_iid, "hist_len":hist_len}
+                    'hist_item': hist_iid, "hist_len": hist_len}
 
-    #feature_names = get_feature_names(feature_columns)
+    # feature_names = get_feature_names(feature_columns)
     x = feature_dict
-    y = np.array([1, 1, 1,1])
-    return x, y, user_feature_columns,item_feature_columns
+    y = np.array([1, 1, 1, 1])
+    return x, y, user_feature_columns, item_feature_columns
 
 
 def get_xy_fd_sdm(hash_flag=False):
-
-    user_feature_columns = [SparseFeat('user',3),
+    user_feature_columns = [SparseFeat('user', 3),
                             SparseFeat('gender', 2),
-                            VarLenSparseFeat(SparseFeat('prefer_item', vocabulary_size=100,embedding_dim=8,
-                                                        embedding_name='item'), maxlen=6, length_name="prefer_sess_length"),
+                            VarLenSparseFeat(SparseFeat('prefer_item', vocabulary_size=100, embedding_dim=8,
+                                                        embedding_name='item'), maxlen=6,
+                                             length_name="prefer_sess_length"),
                             VarLenSparseFeat(SparseFeat('prefer_cate', vocabulary_size=100, embedding_dim=8,
-                                                        embedding_name='cate'), maxlen=6, length_name="prefer_sess_length"),
-                            VarLenSparseFeat(SparseFeat('short_item', vocabulary_size=100,embedding_dim=8,
-                                                        embedding_name='item'), maxlen=4, length_name="short_sess_length"),
+                                                        embedding_name='cate'), maxlen=6,
+                                             length_name="prefer_sess_length"),
+                            VarLenSparseFeat(SparseFeat('short_item', vocabulary_size=100, embedding_dim=8,
+                                                        embedding_name='item'), maxlen=4,
+                                             length_name="short_sess_length"),
                             VarLenSparseFeat(SparseFeat('short_cate', vocabulary_size=100, embedding_dim=8,
-                                                        embedding_name='cate'), maxlen=4, length_name="short_sess_length"),
+                                                        embedding_name='cate'), maxlen=4,
+                                             length_name="short_sess_length"),
                             ]
-    item_feature_columns = [SparseFeat('item', 100, embedding_dim=8,)]
-
+    item_feature_columns = [SparseFeat('item', 100, embedding_dim=8, )]
 
     uid = np.array([0, 1, 2, 1])
     ugender = np.array([0, 1, 0, 1])
@@ -419,12 +449,13 @@ def get_xy_fd_sdm(hash_flag=False):
     prefer_len = np.array([6, 5, 4, 3])
     short_len = np.array([3, 3, 2, 1])
 
-    feature_dict = {'user': uid, 'gender': ugender, 'item': iid, 'prefer_item': prefer_iid, "prefer_cate":prefer_cate,
-                    'short_item': short_iid, 'short_cate': short_cate, 'prefer_sess_length': prefer_len, 'short_sess_length':short_len}
+    feature_dict = {'user': uid, 'gender': ugender, 'item': iid, 'prefer_item': prefer_iid, "prefer_cate": prefer_cate,
+                    'short_item': short_iid, 'short_cate': short_cate, 'prefer_sess_length': prefer_len,
+                    'short_sess_length': short_len}
 
-    #feature_names = get_feature_names(feature_columns)
+    # feature_names = get_feature_names(feature_columns)
     x = feature_dict
     y = np.array([1, 1, 1, 0])
     history_feature_list = ['item', 'cate']
 
-    return x, y, user_feature_columns, item_feature_columns, history_feature_list
\ No newline at end of file
+    return x, y, user_feature_columns, item_feature_columns, history_feature_list