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Diffstat (limited to 'tensorflow/docs_src/guide/variables.md')
| -rw-r--r-- | tensorflow/docs_src/guide/variables.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/guide/variables.md b/tensorflow/docs_src/guide/variables.md index cd8c4b5b9a..5d5d73394c 100644 --- a/tensorflow/docs_src/guide/variables.md +++ b/tensorflow/docs_src/guide/variables.md @@ -119,7 +119,7 @@ It is particularly important for variables to be in the correct device in distributed settings. Accidentally putting variables on workers instead of parameter servers, for example, can severely slow down training or, in the worst case, let each worker blithely forge ahead with its own independent copy of each -variable. For this reason we provide @{tf.train.replica_device_setter}, which +variable. For this reason we provide `tf.train.replica_device_setter`, which can automatically place variables in parameter servers. For example: ``` python @@ -211,7 +211,7 @@ sess.run(assignment) # or assignment.op.run(), or assignment.eval() Most TensorFlow optimizers have specialized ops that efficiently update the values of variables according to some gradient descent-like algorithm. See -@{tf.train.Optimizer} for an explanation of how to use optimizers. +`tf.train.Optimizer` for an explanation of how to use optimizers. Because variables are mutable it's sometimes useful to know what version of a variable's value is being used at any point in time. To force a re-read of the |