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The following commit(s) were added to refs/heads/master by this push:
new 5e587e5 fix w&d (#9303)
5e587e5 is described below
commit 5e587e59dbefd7f0e399791c4ecedead0b1e46ce
Author: Ziyue Huang <[email protected]>
AuthorDate: Fri Jan 5 02:01:30 2018 +0800
fix w&d (#9303)
---
example/sparse/wide_deep/README.md | 2 +-
example/sparse/wide_deep/model.py | 4 ++--
2 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/example/sparse/wide_deep/README.md
b/example/sparse/wide_deep/README.md
index a538106..3df5e42 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 e8ba531..b907455 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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