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/data/train/sample_train.txt

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+0	7	31		1	239	1	8	9	49	1	2	0	1	68fd1e64	8084ee93	d032c263	c18be181	43b19349	fe6b92e5	cee47266	0b153874	a73ee510	14781fa9	87fe3e10	dfbb09fb	3bd6c21d	b28479f6	16d2748c	84898b2a	27c07bd6	003d4f4f			0014c32a		32c7478e	3b183c5c		
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+0		259	4		103468		0	0	14		0			05db9164	8947f767	d8ec4c68	ac1667dd	4cf72387	7e0ccccf	3527bb7c	0b153874	7cc72ec2	3b08e48b	2b9f131d	2a63b3ee	aca10c14	07d13a8f	2c14c412	11b43c2e	8efede7f	bd17c3da	21ddcdc9	a458ea53	79a05ba5		32c7478e	4fb9fee0	010f6491	004f1180
+1	3	145		4	108	6	4	4	31	1	2		4	8cf07265	6c2cbbdc	a42bd759	8b3b6b2e	25c83c98		f00bddf8	062b5529	a73ee510	0d538fca	55795b33	6bb7b021	39795005	64c94865	af094307	c3815fe3	e5ba7672	fb299884			987d0b7a		32c7478e	145ae095		
+1		147		1	159966		0	1	1		0		1	68fd1e64	38d50e09	c86b2d8d	657dc3b9	25c83c98	7e0ccccf	bc324536	1f89b562	7cc72ec2	474773a7	2bcfb78f	1ca7a526	e6fc496d	b28479f6	06373944	ba46c3a1	e5ba7672	fffe2a63	21ddcdc9	b1252a9d	eb0fc6f8	ad3062eb	32c7478e	df487a73	001f3601	c27f155b

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'