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Merged
merged 4 commits into from
Jun 16, 2021
Merged

Update regression.ipynb #1908

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1475be6
Update regression.ipynb
rmothukuru Jun 15, 2021
3d63aa7
Mention the one-hot features in the note.
MarkDaoust Jun 15, 2021
e014131
Update site/en/tutorials/keras/regression.ipynb
8bitmp3 Jun 15, 2021
e1f4792
Fix missing /n in regression.ipynb, nblint, nbfmt
8bitmp3 Jun 15, 2021
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6 changes: 4 additions & 2 deletions site/en/tutorials/keras/regression.ipynb
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Expand Up @@ -290,7 +290,7 @@
"\n",
"Now split the dataset into a training set and a test set.\n",
"\n",
"Use the test set in the final evaluation of our models."
"Use the test set in the final evaluation of your models."
]
},
{
Expand Down Expand Up @@ -407,7 +407,9 @@
"\n",
"One reason this is important is because the features are multiplied by the model weights. So the scale of the outputs and the scale of the gradients are affected by the scale of the inputs. \n",
"\n",
"Although a model *might* converge without feature normalization, normalization makes training much more stable. "
"Although a model *might* converge without feature normalization, normalization makes training much more stable.\n",
"\n",
"Note: There is no advantage to normalizing the one-hot features, it is done here for simplicity. For more details on how to use the preprocessing layers, refer the [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) guide and the [Classify structured data using Keras preprocessing layers](https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers) tutorial."
]
},
{
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