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Posted to commits@mxnet.apache.org by jx...@apache.org on 2018/03/20 17:06:51 UTC

[incubator-mxnet] branch master updated: Update linear-regression.md (#10129)

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jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
     new 959da84  Update linear-regression.md (#10129)
959da84 is described below

commit 959da8401ec69d047d1de092a932341d9418469b
Author: Kai Li <11...@qq.com>
AuthorDate: Wed Mar 21 01:06:47 2018 +0800

    Update linear-regression.md (#10129)
    
    * Update linear-regression.md
    
    * Update linear-regression.md
    
    * Update linear-regression.md
    
    * Update linear-regression.md
---
 docs/tutorials/python/linear-regression.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/tutorials/python/linear-regression.md b/docs/tutorials/python/linear-regression.md
index 9dfcf07..0a5e308 100644
--- a/docs/tutorials/python/linear-regression.md
+++ b/docs/tutorials/python/linear-regression.md
@@ -49,8 +49,8 @@ tells the iterator to randomize the order in which examples are shown to the mod
 
 
 ```python
-train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label')
-eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
+train_iter = mx.io.NDArrayIter(train_data, train_label, batch_size, shuffle=True, label_name='lin_reg_label')
+eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False, label_name='lin_reg_label')
 ```
 
 In the above example, we have made use of `NDArrayIter`, which is useful for iterating
@@ -184,7 +184,7 @@ Let us try and add some noise to the evaluation data and see how the MSE changes
 ```python
 eval_data = np.array([[7,2],[6,10],[12,2]])
 eval_label = np.array([11.1,26.1,16.1]) #Adding 0.1 to each of the values
-eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
+eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False, label_name='lin_reg_label')
 model.score(eval_iter, metric)
 ```
 

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