diff --git a/docs/source/models/loss.md b/docs/source/models/loss.md
index e5d81bae8..881794e6a 100644
--- a/docs/source/models/loss.md
+++ b/docs/source/models/loss.md
@@ -156,6 +156,58 @@ EasyRec支持两种损失函数配置方式：1）使用单个损失函数；2
 - loss_weight_strategy: Random
   - 表示损失函数的权重设定为归一化的随机数
 
+### Loss动态权重
+
+在多目标学习任务中，我们经常遇到给与不同目标设置不同的权重，甚至不同目标的loss权重会随着样本而动态变化。EasyRec支持用户为不同目标设置动态的权重。
+
+- 1.首先在dataset中配置动态权重字段的名称，同时增加这些字段的input_config，如下示例
+
+```protobuf
+data_config {
+  batch_size: 4096
+  label_fields: "clk"
+  label_fields: "buy"
+  label_dynamic_weight: "clk_weight"
+  label_dynamic_weight: "buy_weight"
+  prefetch_size: 32
+  input_type: CSVInput
+  input_fields {
+    input_name: "clk"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "buy"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "clk_weight"
+    input_type: double
+  }
+  input_fields {
+    input_name: "buy_weight"
+    input_type: double
+  }
+}
+```
+
+- 2.需要在对应的任务tower中设置对应的权重列，如下示例
+
+```protobuf
+task_towers {
+  tower_name: "ctr"
+  label_name: "clk"
+  dnn {
+    hidden_units: [256, 192, 128, 64]
+  }
+  num_class: 1
+  dynamic_weight: "clk_weight"
+  loss_type: CLASSIFICATION
+  metrics_set: {
+   auc {}
+  }
+}
+```
+
 ### 参考论文：
 
 - 《 Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics 》
diff --git a/easy_rec/python/input/input.py b/easy_rec/python/input/input.py
index d94b1de13..35c4eab32 100644
--- a/easy_rec/python/input/input.py
+++ b/easy_rec/python/input/input.py
@@ -78,6 +78,7 @@ def __init__(self,
         x.default_val for x in data_config.input_fields
     ]
     self._label_fields = list(data_config.label_fields)
+    self._label_dynamic_weight = list(data_config.label_dynamic_weight)
     self._feature_fields = list(data_config.feature_fields)
     self._label_sep = list(data_config.label_sep)
     self._label_dim = list(data_config.label_dim)
@@ -139,6 +140,8 @@ def __init__(self,
     # add sample weight to effective fields
     if self._data_config.HasField('sample_weight'):
       self._effective_fields.append(self._data_config.sample_weight)
+    if len(self._label_dynamic_weight) > 0:
+      self._effective_fields.extend(self._label_dynamic_weight)
 
     # add uid_field of GAUC and session_fields of SessionAUC
     if self._pipeline_config is not None:
@@ -234,6 +237,7 @@ def get_feature_input_fields(self):
     return [
         x for x in self._input_fields
         if x not in self._label_fields and x != self._data_config.sample_weight
+        and x not in self._label_dynamic_weight
     ]
 
   def should_stop(self, curr_epoch):
@@ -269,13 +273,14 @@ def create_multi_placeholders(self, export_config):
       effective_fids = [
           fid for fid in range(len(self._input_fields))
           if self._input_fields[fid] not in self._label_fields and
+          self._input_fields[fid] not in self._label_dynamic_weight and
           self._input_fields[fid] != sample_weight_field
       ]
 
     inputs = {}
     for fid in effective_fids:
       input_name = self._input_fields[fid]
-      if input_name == sample_weight_field:
+      if input_name == sample_weight_field or input_name in self._label_dynamic_weight:
         continue
       if placeholder_named_by_input:
         placeholder_name = input_name
@@ -318,6 +323,7 @@ def create_placeholders(self, export_config):
       effective_fids = [
           fid for fid in range(len(self._input_fields))
           if self._input_fields[fid] not in self._label_fields and
+          self._input_fields[fid] not in self._label_dynamic_weight and
           self._input_fields[fid] != sample_weight_field
       ]
       logging.info(
@@ -330,6 +336,8 @@ def create_placeholders(self, export_config):
       ftype = self._input_field_types[fid]
       tf_type = get_tf_type(ftype)
       input_name = self._input_fields[fid]
+      if input_name in self._label_dynamic_weight:
+        continue
       if tf_type in [tf.float32, tf.double, tf.int32, tf.int64]:
         features[input_name] = tf.string_to_number(
             input_vals[:, tmp_id],
@@ -925,6 +933,14 @@ def _preprocess(self, field_dict):
       if self._mode != tf.estimator.ModeKeys.PREDICT:
         parsed_dict[constant.SAMPLE_WEIGHT] = field_dict[
             self._data_config.sample_weight]
+    if len(self._label_dynamic_weight
+           ) > 0 and self._mode != tf.estimator.ModeKeys.PREDICT:
+      for label_weight in self._label_dynamic_weight:
+        if field_dict[label_weight].dtype == tf.float32:
+          parsed_dict[label_weight] = field_dict[label_weight]
+        else:
+          parsed_dict[label_weight] = tf.cast(
+              field_dict[label_weight], dtype=tf.float64)
 
     if Input.DATA_OFFSET in field_dict:
       parsed_dict[Input.DATA_OFFSET] = field_dict[Input.DATA_OFFSET]
diff --git a/easy_rec/python/model/multi_task_model.py b/easy_rec/python/model/multi_task_model.py
index fa6ce8948..f38d825a1 100644
--- a/easy_rec/python/model/multi_task_model.py
+++ b/easy_rec/python/model/multi_task_model.py
@@ -224,6 +224,8 @@ def build_loss_graph(self):
     for task_tower_cfg in self._task_towers:
       tower_name = task_tower_cfg.tower_name
       loss_weight = task_tower_cfg.weight
+      if task_tower_cfg.HasField('dynamic_weight'):
+        loss_weight *= self._feature_dict[task_tower_cfg.dynamic_weight]
       if task_tower_cfg.use_sample_weight:
         loss_weight *= self._sample_weight
 
diff --git a/easy_rec/python/protos/dataset.proto b/easy_rec/python/protos/dataset.proto
index 5ffefd064..2b64da65d 100644
--- a/easy_rec/python/protos/dataset.proto
+++ b/easy_rec/python/protos/dataset.proto
@@ -292,6 +292,8 @@ message DatasetConfig {
 
     // input field for sample weight
     optional string sample_weight = 22;
+    // input field for label dynimic weight
+    repeated string label_dynamic_weight = 27;
     // the compression type of tfrecord
     optional string data_compression_type = 23 [default = ''];
 
diff --git a/easy_rec/python/protos/tower.proto b/easy_rec/python/protos/tower.proto
index 14cf64c63..d52c44f5c 100644
--- a/easy_rec/python/protos/tower.proto
+++ b/easy_rec/python/protos/tower.proto
@@ -26,6 +26,8 @@ message TaskTower {
     optional DNN dnn = 6;
     // training loss weights
     optional float weight = 7 [default = 1.0];
+    // training loss label dynamic weights
+    optional string dynamic_weight = 8;
     // label name for indicating the sample space for the task tower
     optional string task_space_indicator_label = 10;
     // the loss weight for sample in the task space
@@ -76,4 +78,7 @@ message BayesTaskTower {
     optional bool use_ait_module = 17 [default = false];
     // set this when the dimensions of last layer of towers are not equal
     optional uint32 ait_project_dim = 18;
+    // training loss label dynamic weights
+    optional string dynamic_weight = 19;
+
 };
diff --git a/easy_rec/python/test/train_eval_test.py b/easy_rec/python/test/train_eval_test.py
index 68d0b8656..a682e91bc 100644
--- a/easy_rec/python/test/train_eval_test.py
+++ b/easy_rec/python/test/train_eval_test.py
@@ -7,11 +7,11 @@
 import threading
 import time
 import unittest
+from distutils.version import LooseVersion
 
 import numpy as np
 import six
 import tensorflow as tf
-from distutils.version import LooseVersion
 from tensorflow.python.platform import gfile
 
 from easy_rec.python.main import predict
@@ -942,12 +942,24 @@ def test_sequence_esmm(self):
         self._test_dir)
     self.assertTrue(self._success)
 
+  def test_label_dynamic_weight_esmm(self):
+    self._success = test_utils.test_single_train_eval(
+        'samples/model_config/esmm_on_label_dynamic_weight_feature_taobao.config',
+        self._test_dir)
+    self.assertTrue(self._success)
+
   def test_sequence_mmoe(self):
     self._success = test_utils.test_single_train_eval(
         'samples/model_config/mmoe_on_sequence_feature_taobao.config',
         self._test_dir)
     self.assertTrue(self._success)
 
+  def test_label_dynamic_weight_sequence_mmoe(self):
+    self._success = test_utils.test_single_train_eval(
+        'samples/model_config/mmoe_on_label_dynamic_weight_sequence_feature_taobao.config',
+        self._test_dir)
+    self.assertTrue(self._success)
+
   def test_sequence_ple(self):
     self._success = test_utils.test_single_train_eval(
         'samples/model_config/ple_on_sequence_feature_taobao.config',
diff --git a/samples/model_config/esmm_on_label_dynamic_weight_feature_taobao.config b/samples/model_config/esmm_on_label_dynamic_weight_feature_taobao.config
new file mode 100644
index 000000000..be13e0d95
--- /dev/null
+++ b/samples/model_config/esmm_on_label_dynamic_weight_feature_taobao.config
@@ -0,0 +1,319 @@
+train_input_path: "data/test/tb_data/taobao_train_data_label_dynamic_weight"
+eval_input_path: "data/test/tb_data/taobao_test_data_label_dynamic_weight"
+model_dir: "experiments/esmm_taobao_ckpt"
+
+train_config {
+  log_step_count_steps: 100
+  optimizer_config: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.001
+          decay_steps: 1000
+          decay_factor: 0.5
+          min_learning_rate: 0.0000001
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  save_checkpoints_steps: 100
+  sync_replicas: True
+  num_steps: 100
+}
+
+eval_config {
+  metrics_set: {
+    auc {}
+  }
+}
+
+data_config {
+  input_fields {
+    input_name:'clk'
+    input_type: INT32
+  }
+  input_fields {
+    input_name:'buy'
+    input_type: INT32
+  }
+  input_fields {
+    input_name: 'pid'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'adgroup_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cate_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'campaign_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'customer'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'brand'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'user_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cms_segid'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'cms_group_id'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'final_gender_code'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'age_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'pvalue_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'shopping_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'occupation'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'new_user_class_level'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'tag_category_list'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'tag_brand_list'
+    input_type: STRING
+  }
+  input_fields {
+    input_name: 'price'
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "clk_w"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "buy_w"
+    input_type: FLOAT
+  }
+  label_fields: 'buy'
+  label_fields: 'clk'
+  label_dynamic_weight: "clk_w"
+  label_dynamic_weight: "buy_w"
+  batch_size: 4096
+  num_epochs: 10000
+  prefetch_size: 32
+  input_type: CSVInput
+}
+
+feature_configs : {
+  input_names: 'pid'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'adgroup_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'cate_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10000
+}
+feature_configs : {
+  input_names: 'campaign_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'customer'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'brand'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'user_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs : {
+  input_names: 'cms_segid'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs : {
+  input_names: 'cms_group_id'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs : {
+  input_names: 'final_gender_code'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'age_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'pvalue_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'shopping_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'occupation'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+  input_names: 'new_user_class_level'
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs : {
+   input_names: 'tag_category_list'
+   feature_type: SequenceFeature
+   separator: '|'
+   hash_bucket_size: 100000
+   embedding_dim: 16
+}
+feature_configs : {
+   input_names: 'tag_brand_list'
+   feature_type: SequenceFeature
+   separator: '|'
+   hash_bucket_size: 100000
+   embedding_dim: 16
+}
+feature_configs : {
+  input_names: 'price'
+  feature_type: IdFeature
+  embedding_dim: 16
+  num_buckets: 50
+}
+model_config: {
+  model_class: 'ESMM'
+  feature_groups: {
+    group_name: 'user'
+    feature_names: 'user_id'
+    feature_names: 'cms_segid'
+    feature_names: 'cms_group_id'
+    feature_names: 'age_level'
+    feature_names: 'pvalue_level'
+    feature_names: 'shopping_level'
+    feature_names: 'occupation'
+    feature_names: 'new_user_class_level'
+    wide_deep: DEEP
+    sequence_features: {
+    group_name: "seq_fea"
+    tf_summary: false
+    allow_key_search:true
+    seq_att_map: {
+       key: "brand"
+       key: "cate_id"
+       hist_seq: "tag_brand_list"
+       hist_seq: "tag_category_list"
+    }
+  }
+  }
+  feature_groups: {
+    group_name: 'item'
+    feature_names: 'adgroup_id'
+    feature_names: 'cate_id'
+    feature_names: 'campaign_id'
+    feature_names: 'customer'
+    feature_names: 'brand'
+    feature_names: 'price'
+    wide_deep: DEEP
+  }
+  esmm {
+    groups {
+      input: "user"
+      dnn {
+        hidden_units: [256, 128, 96, 64]
+      }
+    }
+    groups {
+      input: "item"
+      dnn {
+        hidden_units: [256, 128, 96, 64]
+      }
+    }
+    cvr_tower {
+      tower_name: "cvr"
+      label_name: "buy"
+      dynamic_weight: "buy_w"
+      dnn {
+        hidden_units: [128, 96, 64, 32, 16]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    ctr_tower {
+      tower_name: "ctr"
+      label_name: "clk"
+      dynamic_weight: "clk_w"
+      dnn {
+        hidden_units: [128, 96, 64, 32, 16]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    l2_regularization: 1e-6
+  }
+  embedding_regularization: 5e-5
+}
diff --git a/samples/model_config/mmoe_on_label_dynamic_weight_sequence_feature_taobao.config b/samples/model_config/mmoe_on_label_dynamic_weight_sequence_feature_taobao.config
new file mode 100644
index 000000000..9226cddb7
--- /dev/null
+++ b/samples/model_config/mmoe_on_label_dynamic_weight_sequence_feature_taobao.config
@@ -0,0 +1,304 @@
+train_input_path: "data/test/tb_data/taobao_train_data_label_dynamic_weight"
+eval_input_path: "data/test/tb_data/taobao_test_data_label_dynamic_weight"
+model_dir: "experiments/mmoe_taobao_ckpt"
+
+train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        exponential_decay_learning_rate {
+          initial_learning_rate: 0.001
+          decay_steps: 1000
+          decay_factor: 0.5
+          min_learning_rate: 1e-07
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  num_steps: 5000
+  sync_replicas: true
+  save_checkpoints_steps: 100
+  log_step_count_steps: 100
+}
+eval_config {
+  metrics_set {
+    auc {
+    }
+  }
+}
+data_config {
+  batch_size: 4096
+  label_fields: "clk"
+  label_fields: "buy"
+  label_dynamic_weight: "clk_w"
+  label_dynamic_weight: "buy_w"
+  prefetch_size: 32
+  input_type: CSVInput
+  input_fields {
+    input_name: "clk"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "buy"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "pid"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "adgroup_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cate_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "campaign_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "customer"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "brand"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "user_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cms_segid"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "cms_group_id"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "final_gender_code"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "age_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "pvalue_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "shopping_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "occupation"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "new_user_class_level"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "tag_category_list"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "tag_brand_list"
+    input_type: STRING
+  }
+  input_fields {
+    input_name: "price"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "clk_w"
+    input_type: INT32
+  }
+  input_fields {
+    input_name: "buy_w"
+    input_type: FLOAT
+  }
+}
+feature_configs {
+  input_names: "pid"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "adgroup_id"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs {
+  input_names: "cate_id"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10000
+}
+feature_configs {
+  input_names: "campaign_id"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs {
+  input_names: "customer"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs {
+  input_names: "brand"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs {
+  input_names: "user_id"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+}
+feature_configs {
+  input_names: "cms_segid"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs {
+  input_names: "cms_group_id"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 100
+}
+feature_configs {
+  input_names: "final_gender_code"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "age_level"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "pvalue_level"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "shopping_level"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "occupation"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "new_user_class_level"
+  feature_type: IdFeature
+  embedding_dim: 16
+  hash_bucket_size: 10
+}
+feature_configs {
+  input_names: "tag_category_list"
+  feature_type: SequenceFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+  separator: "|"
+}
+feature_configs {
+  input_names: "tag_brand_list"
+  feature_type: SequenceFeature
+  embedding_dim: 16
+  hash_bucket_size: 100000
+  separator: "|"
+}
+feature_configs {
+  input_names: "price"
+  feature_type: IdFeature
+  embedding_dim: 16
+  num_buckets: 50
+}
+model_config {
+  model_class: "MMoE"
+  feature_groups {
+    group_name: "all"
+    feature_names: "user_id"
+    feature_names: "cms_segid"
+    feature_names: "cms_group_id"
+    feature_names: "age_level"
+    feature_names: "pvalue_level"
+    feature_names: "shopping_level"
+    feature_names: "occupation"
+    feature_names: "new_user_class_level"
+    feature_names: "adgroup_id"
+    feature_names: "cate_id"
+    feature_names: "campaign_id"
+    feature_names: "customer"
+    feature_names: "brand"
+    feature_names: "price"
+    feature_names: "pid"
+    wide_deep: DEEP
+    sequence_features: {
+    group_name: "seq_fea"
+    tf_summary: false
+    seq_att_map: {
+       key: "brand"
+       key: "cate_id"
+       hist_seq: "tag_brand_list"
+       hist_seq: "tag_category_list"
+    }
+  }
+  }
+  mmoe {
+    expert_dnn {
+      hidden_units: [256, 192, 128, 64]
+    }
+    num_expert: 4
+    task_towers {
+      tower_name: "ctr"
+      label_name: "clk"
+	  dynamic_weight: "clk_w"
+      dnn {
+        hidden_units: [256, 192, 128, 64]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    task_towers {
+      tower_name: "cvr"
+      label_name: "buy"
+	  dynamic_weight: "buy_w"
+      dnn {
+        hidden_units: [256, 192, 128, 64]
+      }
+      num_class: 1
+      weight: 1.0
+      loss_type: CLASSIFICATION
+      metrics_set: {
+       auc {}
+      }
+    }
+    l2_regularization: 1e-06
+  }
+  embedding_regularization: 5e-05
+}