Adjust the dense structure in the wide&deep multi-table

model_zoo/official/recommend/wide_and_deep_multitable/src/config.py

@@ -32,6 +32,7 @@ def argparse_init():
     parser.add_argument("--ftrl_lr", type=float, default=0.1)  # The ftrl lr.
     parser.add_argument("--l2_coef", type=float, default=0.0)  # The l2 coefficient.
     parser.add_argument("--is_tf_dataset", type=bool, default=True)  # The l2 coefficient.
+    parser.add_argument("--dropout_flag", type=int, default=1) # The dropout rate
 
     parser.add_argument("--output_path", type=str, default="./output/")  # The location of the output file.
     parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")  # The location of the checkpoints file.
@@ -83,7 +84,6 @@ class WideDeepConfig():
         self.weight_bias_init = ['normal', 'normal']
         self.emb_init = 'normal'
         self.init_args = [-0.01, 0.01]
-        self.dropout_flag = False
         self.l2_coef = args.l2_coef
         self.ftrl_lr = args.ftrl_lr
         self.adam_lr = args.adam_lr
@@ -93,3 +93,4 @@ class WideDeepConfig():
         self.eval_file_name = args.eval_file_name
         self.loss_file_name = args.loss_file_name
         self.ckpt_path = args.ckpt_path
+        self.dropout_flag = bool(args.dropout_flag)

model_zoo/official/recommend/wide_and_deep_multitable/src/wide_and_deep.py

@@ -89,9 +89,11 @@ class DenseLayer(nn.Cell):
                  output_dim,
                  weight_bias_init,
                  act_str,
-                 keep_prob=0.7,
+                 keep_prob=0.8,
                  scale_coef=1.0,
-                 convert_dtype=True):
+                 use_activation=True,
+                 convert_dtype=True,
+                 drop_out=False):
         super(DenseLayer, self).__init__()
         weight_init, bias_init = weight_bias_init
         self.weight = init_method(weight_init, [input_dim, output_dim],
@@ -101,11 +103,13 @@ class DenseLayer(nn.Cell):
         self.matmul = P.MatMul(transpose_b=False)
         self.bias_add = P.BiasAdd()
         self.cast = P.Cast()
-        self.dropout = Dropout(keep_prob=0.8)
+        self.dropout = Dropout(keep_prob=keep_prob)
         self.mul = P.Mul()
         self.realDiv = P.RealDiv()
         self.scale_coef = scale_coef
+        self.use_activation = use_activation
         self.convert_dtype = convert_dtype
+        self.drop_out = drop_out
 
     def _init_activation(self, act_str):
         act_str = act_str.lower()
@@ -118,23 +122,26 @@ class DenseLayer(nn.Cell):
         return act_func
 
     def construct(self, x):
-        """
-        DenseLayer construct
-        """
-        x = self.act_func(x)
-        if self.training:
+        '''
+        Construct Dense layer
+        '''
+        if self.training and self.drop_out:
             x = self.dropout(x)
-        x = self.mul(x, self.scale_coef)
         if self.convert_dtype:
             x = self.cast(x, mstype.float16)
             weight = self.cast(self.weight, mstype.float16)
+            bias = self.cast(self.bias, mstype.float16)
             wx = self.matmul(x, weight)
+            wx = self.bias_add(wx, bias)
+            if self.use_activation:
+                wx = self.act_func(wx)
             wx = self.cast(wx, mstype.float32)
         else:
             wx = self.matmul(x, self.weight)
-        wx = self.realDiv(wx, self.scale_coef)
-        output = self.bias_add(wx, self.bias)
-        return output
+            wx = self.bias_add(wx, self.bias)
+            if self.use_activation:
+                wx = self.act_func(wx)
+        return wx
 
 
 class WideDeepModel(nn.Cell):
@@ -211,33 +218,40 @@ class WideDeepModel(nn.Cell):
                                         self.all_dim_list[1],
                                         self.weight_bias_init,
                                         self.deep_layer_act,
+                                        drop_out=config.dropout_flag,
                                         convert_dtype=True)
         self.dense_layer_2 = DenseLayer(self.all_dim_list[1],
                                         self.all_dim_list[2],
                                         self.weight_bias_init,
                                         self.deep_layer_act,
+                                        drop_out=config.dropout_flag,
                                         convert_dtype=True)
         self.dense_layer_3 = DenseLayer(self.all_dim_list[2],
                                         self.all_dim_list[3],
                                         self.weight_bias_init,
                                         self.deep_layer_act,
+                                        drop_out=config.dropout_flag,
                                         convert_dtype=True)
         self.dense_layer_4 = DenseLayer(self.all_dim_list[3],
                                         self.all_dim_list[4],
                                         self.weight_bias_init,
                                         self.deep_layer_act,
+                                        drop_out=config.dropout_flag,
                                         convert_dtype=True)
         self.dense_layer_5 = DenseLayer(self.all_dim_list[4],
                                         self.all_dim_list[5],
                                         self.weight_bias_init,
                                         self.deep_layer_act,
+                                        drop_out=config.dropout_flag,
                                         convert_dtype=True)
 
         self.deep_predict = DenseLayer(self.all_dim_list[5],
                                        self.all_dim_list[6],
                                        self.weight_bias_init,
                                        self.deep_layer_act,
-                                       convert_dtype=True)
+                                       drop_out=config.dropout_flag,
+                                       convert_dtype=True,
+                                       use_activation=False)
 
         self.gather_v2 = P.GatherV2()
         self.mul = P.Mul()

model_zoo/official/recommend/wide_and_deep_multitable/train_and_eval_distribute.py

@@ -96,9 +96,10 @@ def train_and_eval(config):
                                   keep_checkpoint_max=10)
     ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
                                  directory=config.ckpt_path, config=ckptconfig)
-
-    model.train(epochs, ds_train, callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback,
-                                             callback, ckpoint_cb])
+    callback_list = [TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback]
+    if int(get_rank()) == 0:
+        callback_list.append(ckpoint_cb)
+    model.train(epochs, ds_train, callbacks=callback_list)
 
 
 if __name__ == "__main__":