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Diffstat (limited to 'tensorflow/contrib/layers/python/layers/layers.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/layers.py | 8 |
1 files changed, 6 insertions, 2 deletions
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index f3229a1605..ef2b673074 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -479,8 +479,12 @@ def batch_norm(inputs, Sergey Ioffe, Christian Szegedy - Can be used as a normalizer function for conv2d and fully_connected. - + Can be used as a normalizer function for conv2d and fully_connected. The + normalization is over all but the last dimension if `data_format` is `NHWC` + and all but the second dimension if `data_format` is `NCHW`. In case of a 2D + tensor this corresponds to the batch dimension, while in case of a 4D tensor this + corresponds to the batch and space dimensions. + Note: when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they need to be added as a dependency to the `train_op`. For example: |