eric-haibin-lin closed pull request #9303: fix wide&deep
URL: https://github.com/apache/incubator-mxnet/pull/9303
 
 
   

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diff --git a/example/sparse/wide_deep/README.md 
b/example/sparse/wide_deep/README.md
index a538106216..3df5e420ee 100644
--- a/example/sparse/wide_deep/README.md
+++ b/example/sparse/wide_deep/README.md
@@ -4,4 +4,4 @@ The example demonstrates how to train [wide and deep 
model](https://arxiv.org/ab
 
 The final accuracy should be around 85%.
 
-- `python wide_deep_classification.py`
+- `python train.py`
diff --git a/example/sparse/wide_deep/model.py 
b/example/sparse/wide_deep/model.py
index e8ba5318b5..b90745599c 100644
--- a/example/sparse/wide_deep/model.py
+++ b/example/sparse/wide_deep/model.py
@@ -49,9 +49,9 @@ def wide_deep_model(num_linear_features, num_embed_features, 
num_cont_features,
 
     hidden = mx.symbol.concat(*features, dim=1)
     hidden = mx.symbol.FullyConnected(data=hidden, num_hidden=hidden_units[1])
-    hideen = mx.symbol.Activation(data=hidden, act_type='relu')
+    hidden = mx.symbol.Activation(data=hidden, act_type='relu')
     hidden = mx.symbol.FullyConnected(data=hidden, num_hidden=hidden_units[2])
-    hideen = mx.symbol.Activation(data=hidden, act_type='relu')
+    hidden = mx.symbol.Activation(data=hidden, act_type='relu')
     deep_out = mx.symbol.FullyConnected(data=hidden, num_hidden=2)
 
     out = mx.symbol.SoftmaxOutput(linear_out + deep_out, label, name='model')


 

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