diff --git a/examples/main_adult.py b/examples/main_adult.py
index 8dc438b92bdba078f50b47efd1c86ca7f654d995..49fd950c7ff313031bba9017d99405a521d8e54f 100644
--- a/examples/main_adult.py
+++ b/examples/main_adult.py
@@ -9,6 +9,14 @@ from pytorch_widedeep.utils.deep_utils import DeepProcessor
 from pytorch_widedeep.models.wide import Wide
 from pytorch_widedeep.models.deep_dense import DeepDense
 
+from pytorch_widedeep.models.wide_deep import WideDeep
+
+from pytorch_widedeep.initializers import *
+from pytorch_widedeep.optimizers import *
+from pytorch_widedeep.lr_schedulers import *
+from pytorch_widedeep.callbacks import *
+from pytorch_widedeep.metrics import *
+
 # use_cuda = torch.cuda.is_available()
 
 import pdb
@@ -29,61 +37,46 @@ if __name__ == '__main__':
     cat_embed_cols = [('education',10), ('relationship',8), ('workclass',10),
         ('occupation',10),('native_country',10)]
     continuous_cols = ["age","hours_per_week"]
+    target = 'income_label'
 
+    target = df[target].values
     prepare_wide = WideProcessor(wide_cols=wide_cols, crossed_cols=crossed_cols)
     X_wide = prepare_wide.fit_transform(df)
-
     prepare_deep = DeepProcessor(embed_cols=cat_embed_cols, continuous_cols=continuous_cols)
     X_deep = prepare_deep.fit_transform(df)
 
-    wide = Wide(X_wide.shape[1], 1)
-    pred_wide = wide(torch.tensor(X_wide[:10]))
-
-    deep = DeepDense(
+    wide = Wide(
+        wide_dim=X_wide.shape[1],
+        output_dim=1)
+    deepdense = DeepDense(
         hidden_layers=[32,16],
         dropout=[0.5],
         deep_column_idx=prepare_deep.deep_column_idx,
         embed_input=prepare_deep.embeddings_input,
         continuous_cols=continuous_cols,
-        batchnorm=True,
         output_dim=1)
-    pred_deep = deep(torch.tensor(X_deep[:10]))
+    model = WideDeep(wide=wide, deepdense=deepdense)
+
+    initializers = {'wide': Normal, 'deepdense':Normal}
+    optimizers = {'wide': Adam, 'deepdense':RAdam(lr=0.001)}
+    schedulers = {'wide': StepLR(step_size=5), 'deepdense':StepLR(step_size=5)}
+
+    callbacks = [EarlyStopping, ModelCheckpoint(filepath='../model_weights/wd_out')]
+    metrics = [BinaryAccuracy]
+
+    model.compile(
+        method='logistic',
+        initializers=initializers,
+        optimizers=optimizers,
+        lr_schedulers=schedulers,
+        callbacks=callbacks,
+        metrics=metrics)
+
+    model.fit(
+        X_wide=X_wide,
+        X_deep=X_deep,
+        target=target,
+        n_epochs=10,
+        batch_size=256,
+        val_split=0.2)
     pdb.set_trace()
-
-
-    # wd_dataset = prepare_data(df,
-    #     target=target,
-    #     wide_cols=wide_cols,
-    #     crossed_cols=crossed_cols,
-    #     cat_embed_cols=cat_embed_cols,
-    #     continuous_cols=continuous_cols)
-
-    # model = WideDeep(
-    #     output_dim=1,
-    #     wide_dim=wd_dataset.wide.shape[1],
-    #     cat_embed_input = wd_dataset.cat_embed_input,
-    #     continuous_cols=wd_dataset.continuous_cols,
-    #     deep_column_idx=wd_dataset.deep_column_idx)
-
-    # initializers = {'wide': Normal, 'deepdense':Normal}
-    # optimizers = {'wide': Adam, 'deepdense':RAdam(lr=0.001)}
-    # schedulers = {'wide': StepLR(step_size=5), 'deepdense':StepLR(step_size=5)}
-
-    # callbacks = [EarlyStopping, ModelCheckpoint(filepath='../model_weights/wd_out.pt')]
-    # metrics = [BinaryAccuracy]
-
-    # model.compile(
-    #     method='logistic',
-    #     initializers=initializers,
-    #     optimizers=optimizers,
-    #     lr_schedulers=schedulers,
-    #     callbacks=callbacks,
-    #     metrics=metrics)
-
-    # model.fit(
-    #     X_wide=wd_dataset.wide,
-    #     X_deep=wd_dataset.deepdense,
-    #     target=wd_dataset.target,
-    #     n_epochs=5,
-    #     batch_size=256,
-    #     val_split=0.2)
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