fix ctr-dnn local training

examples/ctr-dnn_train.yaml

@@ -34,7 +34,7 @@ train:
 
   reader:
     mode: "dataset"
-    batch_size: 32
+    batch_size: 2 
     pipe_command: "python /paddle/eleps/models/ctr_dnn/dataset.py"
     train_data_path: "/paddle/eleps/models/ctr_dnn/data/train"
 
@@ -45,10 +45,7 @@ train:
       sparse_feature_number: 1000001
       sparse_feature_dim: 8
       dense_input_dim: 13
-      fc_sizes: [101, 512, 32] 
-      #  - 1024
-      #  - 512
-      #  - 32
+      fc_sizes: [512, 256, 128, 32] 
       learning_rate: 0.001
 
   save:

models/ctr_dnn/dataset.py

@@ -11,6 +11,8 @@
 # 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.
+from __future__ import print_function
+import sys
 
 import paddle.fluid.incubate.data_generator as dg
 
@@ -37,6 +39,7 @@ class CriteoDataset(dg.MultiSlotDataGenerator):
             This function needs to be implemented by the user, based on data format
             """
             features = line.rstrip('\n').split('\t')
+
             dense_feature = []
             sparse_feature = []
             for idx in continuous_range_:
@@ -46,6 +49,7 @@ class CriteoDataset(dg.MultiSlotDataGenerator):
                     dense_feature.append(
                         (float(features[idx]) - cont_min_[idx - 1]) /
                         cont_diff_[idx - 1])
+
             for idx in categorical_range_:
                 sparse_feature.append(
                     [hash(str(idx) + features[idx]) % hash_dim_])

models/ctr_dnn/model.py

@@ -39,7 +39,7 @@ class Train(object):
                 fluid.layers.data(name="C" + str(i),
                                   shape=[1],
                                   lod_level=1,
-                                  dtype="int64") for i in range(ids)
+                                  dtype="int64") for i in range(1, ids)
             ]
             return sparse_input_ids, [var.name for var in sparse_input_ids]
 
@@ -60,7 +60,7 @@ class Train(object):
         self.label_input, self.label_input_varname = label_input()
 
     def input_vars(self):
-        return self.sparse_inputs + [self.dense_input] + [self.label_input]
+        return [self.dense_input] + self.sparse_inputs + [self.label_input]
 
     def input_varnames(self):
         return [input.name for input in self.input_vars()]

trainer/single_train.py

@@ -169,7 +169,7 @@ class SingleTrainerWithDataset(SingleTrainer):
                                         dataset=dataset,
                                         fetch_list=self.metrics,
                                         fetch_info=["auc ", "batch auc"],
-                                        print_period=100)
+                                        print_period=1)
         context['status'] = 'infer_pass'