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Batch norm docs fix applied to _fused_batch_norm as well

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PiperOrigin-RevId: 157530527
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Neal Wu 2017-05-30 16:50:37 -07:00 committed by TensorFlower Gardener
parent abd4aa49a7
commit 5c73d01024
1 changed files with 6 additions and 4 deletions
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10
tensorflow/contrib/layers/python/layers/layers.py
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@ -158,16 +158,18 @@ def _fused_batch_norm(
Can be used as a normalizer function for conv2d and fully_connected. Can be used as a normalizer function for conv2d and fully_connected.
Note: When is_training is True the moving_mean and moving_variance need to be Note: when training, the moving_mean and moving_variance need to be updated.
updated, by default the update_ops are placed in `tf.GraphKeys.UPDATE_OPS` so By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
they need to be added as a dependency to the `train_op`, example: need to be added as a dependency to the `train_op`. For example:
```python
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops): with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss) train_op = optimizer.minimize(loss)
```
One can set updates_collections=None to force the updates in place, but that One can set updates_collections=None to force the updates in place, but that
can have speed penalty, especially in distributed settings. can have a speed penalty, especially in distributed settings.
Args: Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has inputs: A tensor with 2 or more dimensions, where the first dimension has