Merge pull request #21 from yaoxuefeng6/mod_yaml

update rank yaml, model, and add infer option in rank models

models/rank/dcn/config.yaml

@@ -12,43 +12,66 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 10
-  workspace: "paddlerec.models.rank.dcn"
-
-  reader:
-    batch_size: 2
-    train_data_path: "{workspace}/data/sample_data/train"
-    feat_dict_name: "{workspace}/data/vocab"
+
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.dcn"
+
+dataset:
+  - name: train_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
+    sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
+    dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
+  - name: infer_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/infer"
     sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
     dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      cross_num: 2
-      dnn_hidden_units: [128, 128]
-      l2_reg_cross: 0.00005
-      dnn_use_bn: False
-      clip_by_norm: 100.0
-      cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
-      is_sparse: False
-      is_test: False
-      num_field: 39
-      learning_rate: 0.0001
-      act: "relu"
-      optimizer: adam
-
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+hyper_parameters:
+    optimizer:
+        class: Adam
+        learning_rate: 0.0001
+    # 用户自定义配置
+    cross_num: 2
+    dnn_hidden_units: [128, 128]
+    l2_reg_cross: 0.00005
+    dnn_use_bn: False
+    clip_by_norm: 100.0
+    cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
+    is_sparse: False
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+    print_interval: 1
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
+    print_interval: 1
+
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: train_sample
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/dcn/data/sample_data/infer/infer_sample_data

+label:0 I1:0.69314718056 I2:1.60943791243 I3:1.79175946923 I4:0.0 I5:7.23201033166 I6:1.60943791243 I7:2.77258872224 I8:1.09861228867 I9:5.20400668708 I10:0.69314718056 I11:1.09861228867 I12:0 I13:1.09861228867 C1:95 C2:398 C3:0 C4:0 C5:53 C6:1 C7:73 C8:71 C9:3 C10:1974 C11:832 C12:0 C13:875 C14:8 C15:1764 C16:0 C17:5 C18:390 C19:226 C20:1 C21:0 C22:0 C23:8 C24:1759 C25:1 C26:862
+label:0 I1:1.09861228867 I2:1.38629436112 I3:3.80666248977 I4:0.69314718056 I5:4.63472898823 I6:2.19722457734 I7:1.09861228867 I8:1.09861228867 I9:1.60943791243 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.60943791243 C1:95 C2:200 C3:1184 C4:1929 C5:53 C6:4 C7:1477 C8:2 C9:3 C10:1283 C11:1567 C12:1048 C13:271 C14:6 C15:1551 C16:899 C17:1 C18:162 C19:226 C20:2 C21:575 C22:0 C23:8 C24:1615 C25:1 C26:659
+label:0 I1:1.09861228867 I2:1.38629436112 I3:0.69314718056 I4:2.7080502011 I5:6.64378973315 I6:4.49980967033 I7:1.60943791243 I8:1.09861228867 I9:5.50533153593 I10:0.69314718056 I11:1.38629436112 I12:1.38629436112 I13:3.82864139649 C1:123 C2:378 C3:991 C4:197 C5:53 C6:1 C7:689 C8:2 C9:3 C10:245 C11:623 C12:1482 C13:887 C14:21 C15:106 C16:720 C17:3 C18:768 C19:0 C20:0 C21:1010 C22:1 C23:8 C24:720 C25:0 C26:0
+label:0 I1:0 I2:6.79905586206 I3:0 I4:0 I5:8.38776764398 I6:0 I7:0.0 I8:0.0 I9:0.0 I10:0 I11:0.0 I12:0 I13:0 C1:95 C2:227 C3:0 C4:219 C5:53 C6:4 C7:3174 C8:2 C9:3 C10:569 C11:1963 C12:0 C13:1150 C14:21 C15:1656 C16:0 C17:6 C18:584 C19:0 C20:0 C21:0 C22:0 C23:8 C24:954 C25:0 C26:0
+label:0 I1:1.38629436112 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.38629436112 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:121 C2:147 C3:0 C4:1356 C5:53 C6:7 C7:2120 C8:2 C9:3 C10:703 C11:1678 C12:1210 C13:1455 C14:8 C15:538 C16:1276 C17:6 C18:346 C19:0 C20:0 C21:944 C22:0 C23:10 C24:355 C25:0 C26:0
+label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.45915167004 I6:0 I7:0.0 I8:0.0 I9:1.94591014906 I10:0 I11:0.0 I12:0 I13:0 C1:14 C2:75 C3:993 C4:480 C5:50 C6:6 C7:1188 C8:2 C9:3 C10:245 C11:1037 C12:1365 C13:1421 C14:21 C15:786 C16:5 C17:2 C18:555 C19:0 C20:0 C21:1408 C22:6 C23:7 C24:753 C25:0 C26:0
+label:0 I1:0 I2:1.60943791243 I3:1.09861228867 I4:0 I5:8.06117135969 I6:0 I7:0.0 I8:0.69314718056 I9:1.09861228867 I10:0 I11:0.0 I12:0 I13:0 C1:139 C2:343 C3:553 C4:828 C5:50 C6:4 C7:0 C8:2 C9:3 C10:245 C11:2081 C12:260 C13:455 C14:21 C15:122 C16:1159 C17:2 C18:612 C19:0 C20:0 C21:1137 C22:0 C23:1 C24:1583 C25:0 C26:0
+label:1 I1:0.69314718056 I2:2.07944154168 I3:1.09861228867 I4:0.0 I5:0.0 I6:0.0 I7:0.69314718056 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:95 C2:227 C3:0 C4:1567 C5:21 C6:7 C7:2496 C8:71 C9:3 C10:1913 C11:2212 C12:0 C13:673 C14:21 C15:1656 C16:0 C17:5 C18:584 C19:0 C20:0 C21:0 C22:0 C23:10 C24:954 C25:0 C26:0
+label:0 I1:0 I2:3.87120101091 I3:1.60943791243 I4:2.19722457734 I5:9.85277303799 I6:5.52146091786 I7:3.36729582999 I8:3.4657359028 I9:4.9558270576 I10:0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:14 C2:14 C3:454 C4:197 C5:53 C6:1 C7:1386 C8:2 C9:3 C10:0 C11:1979 C12:205 C13:214 C14:6 C15:1837 C16:638 C17:5 C18:6 C19:0 C20:0 C21:70 C22:0 C23:10 C24:720 C25:0 C26:0
+label:0 I1:0 I2:3.66356164613 I3:0 I4:0.69314718056 I5:10.4263800775 I6:3.09104245336 I7:0.69314718056 I8:1.09861228867 I9:1.38629436112 I10:0 I11:0.69314718056 I12:0 I13:0.69314718056 C1:14 C2:179 C3:120 C4:746 C5:53 C6:0 C7:1312 C8:2 C9:3 C10:1337 C11:1963 C12:905 C13:1150 C14:21 C15:1820 C16:328 C17:9 C18:77 C19:0 C20:0 C21:311 C22:0 C23:10 C24:89 C25:0 C26:0

models/rank/dcn/model.py

@@ -24,44 +24,21 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def init_network(self):
+    def _init_hyper_parameters(self):
         self.cross_num = envs.get_global_env("hyper_parameters.cross_num",
-                                             None, self._namespace)
+                                             None)
         self.dnn_hidden_units = envs.get_global_env(
-            "hyper_parameters.dnn_hidden_units", None, self._namespace)
+            "hyper_parameters.dnn_hidden_units", None)
         self.l2_reg_cross = envs.get_global_env(
-            "hyper_parameters.l2_reg_cross", None, self._namespace)
+            "hyper_parameters.l2_reg_cross", None)
         self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn",
-                                              None, self._namespace)
+                                              None)
         self.clip_by_norm = envs.get_global_env(
-            "hyper_parameters.clip_by_norm", None, self._namespace)
-        cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num",
-                                           None, self._namespace)
-
-        self.sparse_inputs = self._sparse_data_var[1:]
-        self.dense_inputs = self._dense_data_var
-        self.target_input = self._sparse_data_var[0]
-
-        cat_feat_dims_dict = OrderedDict()
-        for line in open(cat_feat_num):
-            spls = line.strip().split()
-            assert len(spls) == 2
-            cat_feat_dims_dict[spls[0]] = int(spls[1])
-        self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
-        )
+            "hyper_parameters.clip_by_norm", None)
+        self.cat_feat_num = envs.get_global_env(
+            "hyper_parameters.cat_feat_num", None)
         self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
-                                             None, self._namespace)
-
-        self.dense_feat_names = [i.name for i in self.dense_inputs]
-        self.sparse_feat_names = [i.name for i in self.sparse_inputs]
-
-        # {feat_name: dims}
-        self.feat_dims_dict = OrderedDict(
-            [(feat_name, 1) for feat_name in self.dense_feat_names])
-        self.feat_dims_dict.update(self.cat_feat_dims_dict)
-
-        self.net_input = None
-        self.loss = None
+                                             None)
 
     def _create_embedding_input(self):
         # sparse embedding
@@ -121,9 +98,29 @@ class Model(ModelBase):
     def _l2_loss(self, w):
         return fluid.layers.reduce_sum(fluid.layers.square(w))
 
-    def train_net(self):
-        self._init_slots()
-        self.init_network()
+    def net(self, inputs, is_infer=False):
+        self.sparse_inputs = self._sparse_data_var[1:]
+        self.dense_inputs = self._dense_data_var
+        self.target_input = self._sparse_data_var[0]
+
+        cat_feat_dims_dict = OrderedDict()
+        for line in open(self.cat_feat_num):
+            spls = line.strip().split()
+            assert len(spls) == 2
+            cat_feat_dims_dict[spls[0]] = int(spls[1])
+        self.cat_feat_dims_dict = cat_feat_dims_dict if cat_feat_dims_dict else OrderedDict(
+        )
+
+        self.dense_feat_names = [i.name for i in self.dense_inputs]
+        self.sparse_feat_names = [i.name for i in self.sparse_inputs]
+
+        # {feat_name: dims}
+        self.feat_dims_dict = OrderedDict(
+            [(feat_name, 1) for feat_name in self.dense_feat_names])
+        self.feat_dims_dict.update(self.cat_feat_dims_dict)
+
+        self.net_input = None
+        self.loss = None
 
         self.net_input = self._create_embedding_input()
 
@@ -146,6 +143,9 @@ class Model(ModelBase):
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc_var
 
+        if is_infer:
+            self._infer_results["AUC"] = auc_var
+
         # logloss
         logloss = fluid.layers.log_loss(
             self.prob, fluid.layers.cast(
@@ -157,11 +157,7 @@ class Model(ModelBase):
         self.loss = self.avg_logloss + l2_reg_cross_loss
         self._cost = self.loss
 
-    def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
-        return optimizer
-
-    def infer_net(self):
-        self.train_net()
+    #def optimizer(self):
+    #    
+    #    optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
+    #    return optimizer

models/rank/deepfm/config.yaml

@@ -12,39 +12,65 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 10
-  workspace: "paddlerec.models.rank.deepfm"
-
-  reader:
-    batch_size: 2
-    train_data_path: "{workspace}/data/sample_data/train"
-    feat_dict_name: "{workspace}/data/sample_data/feat_dict_10.pkl2"
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.deepfm"
+
+
+dataset:
+  - name: train_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
+    sparse_slots: "label feat_idx"
+    dense_slots: "feat_value:39"
+  - name: infer_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
     sparse_slots: "label feat_idx"
     dense_slots: "feat_value:39"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      sparse_feature_number: 1086460
-      sparse_feature_dim: 9
-      num_field: 39
-      fc_sizes: [400, 400, 400]
-      learning_rate: 0.0001
-      reg: 0.001
-      act: "relu"
-      optimizer: SGD
-
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+hyper_parameters:
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    sparse_feature_number: 1086460
+    sparse_feature_dim: 9
+    num_field: 39
+    fc_sizes: [400, 400, 400]
+    reg: 0.001
+    act: "relu"
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_runner
+    trainer_class: single_train
+    epochs: 2
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+    print_interval: 1
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
+    print_interval: 1
+    
+
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: train_sample
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/deepfm/model.py

@@ -24,42 +24,46 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def deepfm_net(self):
+    def _init_hyper_parameters(self):
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None)
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             None)
+        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+        self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
+                                               None)
+        self.act = envs.get_global_env("hyper_parameters.act", None)
+
+    def net(self, inputs, is_infer=False):
         init_value_ = 0.1
         is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
-        sparse_feature_number = envs.get_global_env(
-            "hyper_parameters.sparse_feature_number", None, self._namespace)
-        sparse_feature_dim = envs.get_global_env(
-            "hyper_parameters.sparse_feature_dim", None, self._namespace)
 
         # ------------------------- network input --------------------------
 
-        num_field = envs.get_global_env("hyper_parameters.num_field", None,
-                                        self._namespace)
-
         raw_feat_idx = self._sparse_data_var[1]
         raw_feat_value = self._dense_data_var[0]
         self.label = self._sparse_data_var[0]
 
         feat_idx = raw_feat_idx
         feat_value = fluid.layers.reshape(
-            raw_feat_value, [-1, num_field, 1])  # None * num_field * 1
+            raw_feat_value, [-1, self.num_field, 1])  # None * num_field * 1
 
-        reg = envs.get_global_env("hyper_parameters.reg", 1e-4,
-                                  self._namespace)
         first_weights_re = fluid.embedding(
             input=feat_idx,
             is_sparse=True,
             is_distributed=is_distributed,
             dtype='float32',
-            size=[sparse_feature_number + 1, 1],
+            size=[self.sparse_feature_number + 1, 1],
             padding_idx=0,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.TruncatedNormalInitializer(
                     loc=0.0, scale=init_value_),
-                regularizer=fluid.regularizer.L1DecayRegularizer(reg)))
+                regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)))
         first_weights = fluid.layers.reshape(
-            first_weights_re, shape=[-1, num_field, 1])  # None * num_field * 1
+            first_weights_re,
+            shape=[-1, self.num_field, 1])  # None * num_field * 1
         y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
                                                 1)
 
@@ -70,16 +74,17 @@ class Model(ModelBase):
             is_sparse=True,
             is_distributed=is_distributed,
             dtype='float32',
-            size=[sparse_feature_number + 1, sparse_feature_dim],
+            size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
             padding_idx=0,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.TruncatedNormalInitializer(
                     loc=0.0,
-                    scale=init_value_ / math.sqrt(float(sparse_feature_dim)))))
+                    scale=init_value_ /
+                    math.sqrt(float(self.sparse_feature_dim)))))
         feat_embeddings = fluid.layers.reshape(
             feat_embeddings_re,
-            shape=[-1, num_field,
-                   sparse_feature_dim])  # None * num_field * embedding_size
+            shape=[-1, self.num_field, self.sparse_feature_dim
+                   ])  # None * num_field * embedding_size
         feat_embeddings = feat_embeddings * feat_value  # None * num_field * embedding_size
 
         # sum_square part
@@ -101,17 +106,13 @@ class Model(ModelBase):
 
         # ------------------------- DNN --------------------------
 
-        layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None,
-                                          self._namespace)
-        act = envs.get_global_env("hyper_parameters.act", None,
-                                  self._namespace)
-        y_dnn = fluid.layers.reshape(feat_embeddings,
-                                     [-1, num_field * sparse_feature_dim])
-        for s in layer_sizes:
+        y_dnn = fluid.layers.reshape(
+            feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
+        for s in self.layer_sizes:
             y_dnn = fluid.layers.fc(
                 input=y_dnn,
                 size=s,
-                act=act,
+                act=self.act,
                 param_attr=fluid.ParamAttr(
                     initializer=fluid.initializer.TruncatedNormalInitializer(
                         loc=0.0, scale=init_value_ / math.sqrt(float(10)))),
@@ -133,21 +134,12 @@ class Model(ModelBase):
 
         self.predict = fluid.layers.sigmoid(y_first_order + y_second_order +
                                             y_dnn)
-
-    def train_net(self):
-        self._init_slots()
-        self.deepfm_net()
-
-        # ------------------------- Cost(logloss) --------------------------
-
         cost = fluid.layers.log_loss(
             input=self.predict, label=fluid.layers.cast(self.label, "float32"))
         avg_cost = fluid.layers.reduce_sum(cost)
 
         self._cost = avg_cost
 
-        # ------------------------- Metric(Auc) --------------------------
-
         predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
         label_int = fluid.layers.cast(self.label, 'int64')
         auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
@@ -155,12 +147,5 @@ class Model(ModelBase):
                                                      slide_steps=0)
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc_var
-
-    def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
-        return optimizer
-
-    def infer_net(self):
-        self.train_net()
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

models/rank/din/config.yaml

@@ -12,40 +12,60 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.din"
 
-  epochs: 10
-  workspace: "paddlerec.models.rank.din"
+dataset:
+  - name: sample_1
+    type: DataLoader
+    batch_size: 5
+    data_path: "{workspace}/data/train_data"
+    data_converter: "{workspace}/reader.py"
+  - name: infer_sample
+    type: DataLoader
+    batch_size: 5
+    data_path: "{workspace}/data/train_data"
+    data_converter: "{workspace}/reader.py"
 
-  reader:
-    batch_size: 2
-    class: "{workspace}/reader.py"
-    train_data_path: "{workspace}/data/train_data"
-    dataset_class: "DataLoader"
+hyper_parameters:
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    use_DataLoader: True
+    item_emb_size: 64
+    cat_emb_size: 64
+    is_sparse: False
+    item_count: 63001
+    cat_count: 801
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      use_DataLoader: True
-      item_emb_size: 64
-      cat_emb_size: 64
-      is_sparse: False
-      config_path: "data/config.txt"
-      fc_sizes: [400, 400, 400]
-      learning_rate: 0.0001
-      reg: 0.001
-      act: "sigmoid"
-      optimizer: SGD
+    act: "sigmoid"
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+
+mode: train_runner
+
+runner:
+  - name: train_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+    print_interval: 1
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: sample_1
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/din/model.py

@@ -22,12 +22,58 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def config_read(self, config_path):
-        with open(config_path, "r") as fin:
-            user_count = int(fin.readline().strip())
-            item_count = int(fin.readline().strip())
-            cat_count = int(fin.readline().strip())
-        return user_count, item_count, cat_count
+    def _init_hyper_parameters(self):
+        self.item_emb_size = envs.get_global_env(
+            "hyper_parameters.item_emb_size", 64)
+        self.cat_emb_size = envs.get_global_env(
+            "hyper_parameters.cat_emb_size", 64)
+        self.act = envs.get_global_env("hyper_parameters.act", "sigmoid")
+        self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
+                                             False)
+        #significant for speeding up the training process
+        self.use_DataLoader = envs.get_global_env(
+            "hyper_parameters.use_DataLoader", False)
+        self.item_count = envs.get_global_env("hyper_parameters.item_count",
+                                              63001)
+        self.cat_count = envs.get_global_env("hyper_parameters.cat_count", 801)
+
+    def input_data(self, is_infer=False, **kwargs):
+        seq_len = -1
+        self.data_var = []
+        hist_item_seq = fluid.data(
+            name="hist_item_seq", shape=[None, seq_len], dtype="int64")
+        self.data_var.append(hist_item_seq)
+
+        hist_cat_seq = fluid.data(
+            name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
+        self.data_var.append(hist_cat_seq)
+
+        target_item = fluid.data(
+            name="target_item", shape=[None], dtype="int64")
+        self.data_var.append(target_item)
+
+        target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
+        self.data_var.append(target_cat)
+
+        label = fluid.data(name="label", shape=[None, 1], dtype="float32")
+        self.data_var.append(label)
+
+        mask = fluid.data(
+            name="mask", shape=[None, seq_len, 1], dtype="float32")
+        self.data_var.append(mask)
+
+        target_item_seq = fluid.data(
+            name="target_item_seq", shape=[None, seq_len], dtype="int64")
+        self.data_var.append(target_item_seq)
+
+        target_cat_seq = fluid.data(
+            name="target_cat_seq", shape=[None, seq_len], dtype="int64")
+        self.data_var.append(target_cat_seq)
+
+        train_inputs = [hist_item_seq] + [hist_cat_seq] + [target_item] + [
+            target_cat
+        ] + [label] + [mask] + [target_item_seq] + [target_cat_seq]
+        return train_inputs
 
     def din_attention(self, hist, target_expand, mask):
         """activation weight"""
@@ -59,104 +105,58 @@ class Model(ModelBase):
         out = fluid.layers.reshape(x=out, shape=[0, hidden_size])
         return out
 
-    def train_net(self):
-        seq_len = -1
-        self.item_emb_size = envs.get_global_env(
-            "hyper_parameters.item_emb_size", 64, self._namespace)
-        self.cat_emb_size = envs.get_global_env(
-            "hyper_parameters.cat_emb_size", 64, self._namespace)
-        self.act = envs.get_global_env("hyper_parameters.act", "sigmoid",
-                                       self._namespace)
-        #item_emb_size = 64
-        #cat_emb_size = 64
-        self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
-                                             False, self._namespace)
-        #significant for speeding up the training process
-        self.config_path = envs.get_global_env(
-            "hyper_parameters.config_path", "data/config.txt", self._namespace)
-        self.use_DataLoader = envs.get_global_env(
-            "hyper_parameters.use_DataLoader", False, self._namespace)
-        user_count, item_count, cat_count = self.config_read(self.config_path)
+    def net(self, inputs, is_infer=False):
+        hist_item_seq = inputs[0]
+        hist_cat_seq = inputs[1]
+        target_item = inputs[2]
+        target_cat = inputs[3]
+        label = inputs[4]
+        mask = inputs[5]
+        target_item_seq = inputs[6]
+        target_cat_seq = inputs[7]
 
         item_emb_attr = fluid.ParamAttr(name="item_emb")
         cat_emb_attr = fluid.ParamAttr(name="cat_emb")
 
-        hist_item_seq = fluid.data(
-            name="hist_item_seq", shape=[None, seq_len], dtype="int64")
-        self._data_var.append(hist_item_seq)
-
-        hist_cat_seq = fluid.data(
-            name="hist_cat_seq", shape=[None, seq_len], dtype="int64")
-        self._data_var.append(hist_cat_seq)
-
-        target_item = fluid.data(
-            name="target_item", shape=[None], dtype="int64")
-        self._data_var.append(target_item)
-
-        target_cat = fluid.data(name="target_cat", shape=[None], dtype="int64")
-        self._data_var.append(target_cat)
-
-        label = fluid.data(name="label", shape=[None, 1], dtype="float32")
-        self._data_var.append(label)
-
-        mask = fluid.data(
-            name="mask", shape=[None, seq_len, 1], dtype="float32")
-        self._data_var.append(mask)
-
-        target_item_seq = fluid.data(
-            name="target_item_seq", shape=[None, seq_len], dtype="int64")
-        self._data_var.append(target_item_seq)
-
-        target_cat_seq = fluid.data(
-            name="target_cat_seq", shape=[None, seq_len], dtype="int64")
-        self._data_var.append(target_cat_seq)
-
-        if self.use_DataLoader:
-            self._data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._data_var,
-                capacity=10000,
-                use_double_buffer=False,
-                iterable=False)
-
         hist_item_emb = fluid.embedding(
             input=hist_item_seq,
-            size=[item_count, self.item_emb_size],
+            size=[self.item_count, self.item_emb_size],
             param_attr=item_emb_attr,
             is_sparse=self.is_sparse)
 
         hist_cat_emb = fluid.embedding(
             input=hist_cat_seq,
-            size=[cat_count, self.cat_emb_size],
+            size=[self.cat_count, self.cat_emb_size],
             param_attr=cat_emb_attr,
             is_sparse=self.is_sparse)
 
         target_item_emb = fluid.embedding(
             input=target_item,
-            size=[item_count, self.item_emb_size],
+            size=[self.item_count, self.item_emb_size],
             param_attr=item_emb_attr,
             is_sparse=self.is_sparse)
 
         target_cat_emb = fluid.embedding(
             input=target_cat,
-            size=[cat_count, self.cat_emb_size],
+            size=[self.cat_count, self.cat_emb_size],
             param_attr=cat_emb_attr,
             is_sparse=self.is_sparse)
 
         target_item_seq_emb = fluid.embedding(
             input=target_item_seq,
-            size=[item_count, self.item_emb_size],
+            size=[self.item_count, self.item_emb_size],
             param_attr=item_emb_attr,
             is_sparse=self.is_sparse)
 
         target_cat_seq_emb = fluid.embedding(
             input=target_cat_seq,
-            size=[cat_count, self.cat_emb_size],
+            size=[self.cat_count, self.cat_emb_size],
             param_attr=cat_emb_attr,
             is_sparse=self.is_sparse)
 
         item_b = fluid.embedding(
             input=target_item,
-            size=[item_count, 1],
+            size=[self.item_count, 1],
             param_attr=fluid.initializer.Constant(value=0.0))
 
         hist_seq_concat = fluid.layers.concat(
@@ -195,12 +195,5 @@ class Model(ModelBase):
                                                      slide_steps=0)
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc_var
-
-    def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
-        return optimizer
-
-    def infer_net(self, parameter_list):
-        self.deepfm_net()
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

models/rank/din/reader.py

@@ -29,8 +29,8 @@ from paddlerec.core.utils import envs
 
 class TrainReader(Reader):
     def init(self):
-        self.train_data_path = envs.get_global_env("train_data_path", None,
-                                                   "train.reader")
+        self.train_data_path = envs.get_global_env(
+            "dataset.sample_1.data_path", None)
         self.res = []
         self.max_len = 0
 
@@ -46,7 +46,8 @@ class TrainReader(Reader):
         fo = open("tmp.txt", "w")
         fo.write(str(self.max_len))
         fo.close()
-        self.batch_size = envs.get_global_env("batch_size", 32, "train.reader")
+        self.batch_size = envs.get_global_env("dataset.sample_1.batch_size",
+                                              32, "train.reader")
         self.group_size = self.batch_size * 20
 
     def _process_line(self, line):

models/rank/readme.md

@@ -56,7 +56,18 @@
 <img align="center" src="../../doc/imgs/din.png">
 <p>
 
-## 使用教程
+## 使用教程(快速开始)
+使用样例数据快速开始，参考[训练](###训练) & [预测](###预测)
+## 使用教程(复现论文)
+为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据，并且调整了batch_size等超参以便在样例数据上更加友好的显示训练&测试日志。如果需要复现readme中的效果请按照如下表格调整batch_size等超参，并使用提供的脚本下载对应数据集以及数据预处理。
+|       模型       |       batch_size      |       thread_num      |       epoch_num      |
+| :------------------: | :--------------------: | :--------------------: | :--------------------: |
+|       DNN        |       1000       |        10       |        1       |
+|       DCN        |       512       |       20       |        2       |
+|       DeepFM     |       100       |       10       |        30       |
+|       DIN        |       32       |       10       |        100       |
+|       Wide&Deep  |       40       |       1       |        40       |
+|       xDeepFM        |       100       |       1       |        10       |
 ### 数据处理
 参考每个模型目录数据下载&预处理脚本
 
@@ -68,11 +79,21 @@ sh run.sh
 
 ### 训练
 ```
-python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
+cd modles/rank/dnn # 进入选定好的排序模型的目录 以DNN为例
+python -m paddlerec.run -m paddlerec.models.rank.dnn # 使用内置配置
+# 如果需要使用自定义配置，config.yaml中workspace需要使用改模型目录的绝对路径
+# 自定义修改超参后，指定配置文件，使用自定义配置
+python -m paddlerec.run -m ./config.yaml
 ```
 ### 预测
 ```
-python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
+# 修改对应模型的config.yaml，mode配置infer_runner
+# 示例: mode: runner1 -> mode: infer_runner
+# infer_runner中 class配置为 class: single_infer
+# 如果训练阶段和预测阶段的模型输入一致，phase不需要改动，复用train的即可
+
+# 修改完config.yaml后 执行:
+python -m paddlerec.run -m ./config.yaml # 以DNN为例
 ```
 
 ## 效果对比
@@ -87,6 +108,7 @@ python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
 |       Census-income Data        |       Wide&Deep       |       0.76195         |       0.90577          |       --          |       --          |
 |       Amazon Product        |       DIN       |       0.47005        |       0.86379         |       --          |       --          |
 
+
 ## 分布式
 ### 模型训练性能 (样本/s)
 |       数据集        |       模型       |       单机        |       同步 (4节点)          |       同步 (8节点)          |  同步 (16节点)          |  同步 (32节点)          |

models/rank/wide_deep/config.yaml

@@ -12,37 +12,59 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.wide_deep"
 
-  epochs: 10
-  workspace: "paddlerec.models.rank.wide_deep"
 
-  reader:
-    batch_size: 2
-    train_data_path: "{workspace}/data/sample_data/train"
+dataset:
+  - name: sample_1
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
     sparse_slots: "label"
     dense_slots: "wide_input:8 deep_input:58"
+  - name: infer_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
+    sparse_slots: "label"
+    dense_slots: "wide_input:8 deep_input:58"
+
+hyper_parameters:
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    hidden1_units: 75
+    hidden2_units: 50
+    hidden3_units: 25
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      hidden1_units: 75
-      hidden2_units: 50
-      hidden3_units: 25
-      learning_rate: 0.0001
-      reg: 0.001
-      act: "relu"
-      optimizer: SGD
-
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: sample_1
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/wide_deep/model.py

@@ -24,6 +24,14 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
+    def _init_hyper_parameters(self):
+        self.hidden1_units = envs.get_global_env(
+            "hyper_parameters.hidden1_units", 75)
+        self.hidden2_units = envs.get_global_env(
+            "hyper_parameters.hidden2_units", 50)
+        self.hidden3_units = envs.get_global_env(
+            "hyper_parameters.hidden3_units", 25)
+
     def wide_part(self, data):
         out = fluid.layers.fc(
             input=data,
@@ -56,21 +64,14 @@ class Model(ModelBase):
 
         return l3
 
-    def train_net(self):
-        self._init_slots()
+    def net(self, inputs, is_infer=False):
         wide_input = self._dense_data_var[0]
         deep_input = self._dense_data_var[1]
         label = self._sparse_data_var[0]
 
-        hidden1_units = envs.get_global_env("hyper_parameters.hidden1_units",
-                                            75, self._namespace)
-        hidden2_units = envs.get_global_env("hyper_parameters.hidden2_units",
-                                            50, self._namespace)
-        hidden3_units = envs.get_global_env("hyper_parameters.hidden3_units",
-                                            25, self._namespace)
         wide_output = self.wide_part(wide_input)
-        deep_output = self.deep_part(deep_input, hidden1_units, hidden2_units,
-                                     hidden3_units)
+        deep_output = self.deep_part(deep_input, self.hidden1_units,
+                                     self.hidden2_units, self.hidden3_units)
 
         wide_model = fluid.layers.fc(
             input=wide_output,
@@ -109,18 +110,12 @@ class Model(ModelBase):
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc
         self._metrics["ACC"] = acc
+        if is_infer:
+            self._infer_results["AUC"] = auc_var
+            self._infer_results["ACC"] = acc
 
         cost = fluid.layers.sigmoid_cross_entropy_with_logits(
             x=prediction, label=fluid.layers.cast(
                 label, dtype='float32'))
         avg_cost = fluid.layers.mean(cost)
         self._cost = avg_cost
-
-    def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
-        return optimizer
-
-    def infer_net(self):
-        self.train_net()

models/rank/xdeepfm/config.yaml

@@ -11,41 +11,61 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+debug: false
+workspace: "paddlerec.models.rank.xdeepfm"
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
-
-  epochs: 10
-  workspace: "paddlerec.models.rank.xdeepfm"
-
-  reader:
-    batch_size: 2
-    train_data_path: "{workspace}/data/sample_data/train"
+dataset:
+  - name: sample_1
+    type: QueueDataset #或者DataLoader
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
+    sparse_slots: "label feat_idx"
+    dense_slots: "feat_value:39"
+  - name: infer_sample
+    type: QueueDataset #或者DataLoader
+    batch_size: 5
+    data_path: "{workspace}/data/sample_data/train"
     sparse_slots: "label feat_idx"
     dense_slots: "feat_value:39"
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      layer_sizes_dnn: [10, 10, 10]
-      layer_sizes_cin: [10, 10]
-      sparse_feature_number: 1086460
-      sparse_feature_dim: 9
-      num_field: 39
-      fc_sizes: [400, 400, 400]
-      learning_rate: 0.0001
-      reg: 0.0001
-      act: "relu"
-      optimizer: SGD
+hyper_parameters:
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    layer_sizes_dnn: [10, 10, 10]
+    layer_sizes_cin: [10, 10]
+    sparse_feature_number: 1086460
+    sparse_feature_dim: 9
+    num_field: 39
+    fc_sizes: [400, 400, 400]
+    act: "relu"
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: sample_1
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/xdeepfm/model.py

@@ -22,38 +22,45 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def xdeepfm_net(self):
+    def _init_hyper_parameters(self):
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None)
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             None)
+        self.layer_sizes_cin = envs.get_global_env(
+            "hyper_parameters.layer_sizes_cin", None)
+        self.layer_sizes_dnn = envs.get_global_env(
+            "hyper_parameters.layer_sizes_dnn", None)
+        self.act = envs.get_global_env("hyper_parameters.act", None)
+
+    def net(self, inputs, is_infer=False):
+        raw_feat_idx = self._sparse_data_var[1]
+        raw_feat_value = self._dense_data_var[0]
+        self.label = self._sparse_data_var[0]
+
         init_value_ = 0.1
         initer = fluid.initializer.TruncatedNormalInitializer(
             loc=0.0, scale=init_value_)
 
         is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
-        sparse_feature_number = envs.get_global_env(
-            "hyper_parameters.sparse_feature_number", None, self._namespace)
-        sparse_feature_dim = envs.get_global_env(
-            "hyper_parameters.sparse_feature_dim", None, self._namespace)
 
         # ------------------------- network input --------------------------
 
-        num_field = envs.get_global_env("hyper_parameters.num_field", None,
-                                        self._namespace)
-        raw_feat_idx = self._sparse_data_var[1]
-        raw_feat_value = self._dense_data_var[0]
-        self.label = self._sparse_data_var[0]
-
         feat_idx = raw_feat_idx
         feat_value = fluid.layers.reshape(
-            raw_feat_value, [-1, num_field, 1])  # None * num_field * 1
+            raw_feat_value, [-1, self.num_field, 1])  # None * num_field * 1
 
         feat_embeddings = fluid.embedding(
             input=feat_idx,
             is_sparse=True,
             dtype='float32',
-            size=[sparse_feature_number + 1, sparse_feature_dim],
+            size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
             padding_idx=0,
             param_attr=fluid.ParamAttr(initializer=initer))
         feat_embeddings = fluid.layers.reshape(feat_embeddings, [
-            -1, num_field, sparse_feature_dim
+            -1, self.num_field, self.sparse_feature_dim
         ])  # None * num_field * embedding_size
         feat_embeddings = feat_embeddings * feat_value  # None * num_field * embedding_size
 
@@ -63,11 +70,11 @@ class Model(ModelBase):
             input=feat_idx,
             is_sparse=True,
             dtype='float32',
-            size=[sparse_feature_number + 1, 1],
+            size=[self.sparse_feature_number + 1, 1],
             padding_idx=0,
             param_attr=fluid.ParamAttr(initializer=initer))
         weights_linear = fluid.layers.reshape(
-            weights_linear, [-1, num_field, 1])  # None * num_field * 1
+            weights_linear, [-1, self.num_field, 1])  # None * num_field * 1
         b_linear = fluid.layers.create_parameter(
             shape=[1],
             dtype='float32',
@@ -77,31 +84,30 @@ class Model(ModelBase):
 
         # -------------------- CIN  --------------------
 
-        layer_sizes_cin = envs.get_global_env(
-            "hyper_parameters.layer_sizes_cin", None, self._namespace)
         Xs = [feat_embeddings]
-        last_s = num_field
-        for s in layer_sizes_cin:
+        last_s = self.num_field
+        for s in self.layer_sizes_cin:
             # calculate Z^(k+1) with X^k and X^0
             X_0 = fluid.layers.reshape(
                 fluid.layers.transpose(Xs[0], [0, 2, 1]),
-                [-1, sparse_feature_dim, num_field,
+                [-1, self.sparse_feature_dim, self.num_field,
                  1])  # None, embedding_size, num_field, 1
             X_k = fluid.layers.reshape(
                 fluid.layers.transpose(Xs[-1], [0, 2, 1]),
-                [-1, sparse_feature_dim, 1,
+                [-1, self.sparse_feature_dim, 1,
                  last_s])  # None, embedding_size, 1, last_s
             Z_k_1 = fluid.layers.matmul(
                 X_0, X_k)  # None, embedding_size, num_field, last_s
 
             # compresses Z^(k+1) to X^(k+1)
             Z_k_1 = fluid.layers.reshape(Z_k_1, [
-                -1, sparse_feature_dim, last_s * num_field
+                -1, self.sparse_feature_dim, last_s * self.num_field
             ])  # None, embedding_size, last_s*num_field
             Z_k_1 = fluid.layers.transpose(
                 Z_k_1, [0, 2, 1])  # None, s*num_field, embedding_size
             Z_k_1 = fluid.layers.reshape(
-                Z_k_1, [-1, last_s * num_field, 1, sparse_feature_dim]
+                Z_k_1,
+                [-1, last_s * self.num_field, 1, self.sparse_feature_dim]
             )  # None, last_s*num_field, 1, embedding_size  (None, channal_in, h, w) 
             X_k_1 = fluid.layers.conv2d(
                 Z_k_1,
@@ -112,7 +118,8 @@ class Model(ModelBase):
                 param_attr=fluid.ParamAttr(
                     initializer=initer))  # None, s, 1, embedding_size
             X_k_1 = fluid.layers.reshape(
-                X_k_1, [-1, s, sparse_feature_dim])  # None, s, embedding_size
+                X_k_1,
+                [-1, s, self.sparse_feature_dim])  # None, s, embedding_size
 
             Xs.append(X_k_1)
             last_s = s
@@ -130,17 +137,13 @@ class Model(ModelBase):
 
         # -------------------- DNN --------------------
 
-        layer_sizes_dnn = envs.get_global_env(
-            "hyper_parameters.layer_sizes_dnn", None, self._namespace)
-        act = envs.get_global_env("hyper_parameters.act", None,
-                                  self._namespace)
-        y_dnn = fluid.layers.reshape(feat_embeddings,
-                                     [-1, num_field * sparse_feature_dim])
-        for s in layer_sizes_dnn:
+        y_dnn = fluid.layers.reshape(
+            feat_embeddings, [-1, self.num_field * self.sparse_feature_dim])
+        for s in self.layer_sizes_dnn:
             y_dnn = fluid.layers.fc(
                 input=y_dnn,
                 size=s,
-                act=act,
+                act=self.act,
                 param_attr=fluid.ParamAttr(initializer=initer),
                 bias_attr=None)
         y_dnn = fluid.layers.fc(input=y_dnn,
@@ -152,11 +155,6 @@ class Model(ModelBase):
         # ------------------- xDeepFM ------------------
 
         self.predict = fluid.layers.sigmoid(y_linear + y_cin + y_dnn)
-
-    def train_net(self):
-        self._init_slots()
-        self.xdeepfm_net()
-
         cost = fluid.layers.log_loss(
             input=self.predict,
             label=fluid.layers.cast(self.label, "float32"),
@@ -172,12 +170,5 @@ class Model(ModelBase):
                                                      slide_steps=0)
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc_var
-
-    def optimizer(self):
-        learning_rate = envs.get_global_env("hyper_parameters.learning_rate",
-                                            None, self._namespace)
-        optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
-        return optimizer
-
-    def infer_net(self):
-        self.train_net()
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

setup.py

@@ -62,7 +62,8 @@ def build(dirname):
 
     models_copy = [
         'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy',
-        'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*'
+        'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*',
+        'data/sample_data/infer/*'
     ]
 
     engine_copy = ['*/*.sh']