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Diffstat (limited to 'tensorflow/docs_src/guide/saved_model.md')
| -rw-r--r-- | tensorflow/docs_src/guide/saved_model.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/docs_src/guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md index c260da7966..6c967fd882 100644 --- a/tensorflow/docs_src/guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -7,7 +7,7 @@ automatically save and restore variables in the `model_dir`. ## Save and restore variables -TensorFlow @{$variables} are the best way to represent shared, persistent state +TensorFlow [Variables](../guide/variables.md) are the best way to represent shared, persistent state manipulated by your program. The `tf.train.Saver` constructor adds `save` and `restore` ops to the graph for all, or a specified list, of the variables in the graph. The `Saver` object provides methods to run these ops, specifying paths @@ -274,7 +274,7 @@ Ops has not changed. The `tf.saved_model.builder.SavedModelBuilder` class allows users to control whether default-valued attributes must be stripped from the -@{$extend/tool_developers#nodes$`NodeDefs`} +[`NodeDefs`](../extend/tool_developers/index.md#nodes) while adding a meta graph to the SavedModel bundle. Both `tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables` and `tf.saved_model.builder.SavedModelBuilder.add_meta_graph` @@ -413,7 +413,7 @@ SavedModel format. This section explains how to: ### Prepare serving inputs -During training, an @{$premade_estimators#input_fn$`input_fn()`} ingests data +During training, an [`input_fn()`](../guide/premade_estimators.md#input_fn) ingests data and prepares it for use by the model. At serving time, similarly, a `serving_input_receiver_fn()` accepts inference requests and prepares them for the model. This function has the following purposes: @@ -616,7 +616,7 @@ result = stub.Classify(request, 10.0) # 10 secs timeout The returned result in this example is a `ClassificationResponse` protocol buffer. -This is a skeletal example; please see the @{$deploy$Tensorflow Serving} +This is a skeletal example; please see the [Tensorflow Serving](../deploy/index.md) documentation and [examples](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example) for more details. @@ -647,7 +647,7 @@ You can use the SavedModel Command Line Interface (CLI) to inspect and execute a SavedModel. For example, you can use the CLI to inspect the model's `SignatureDef`s. The CLI enables you to quickly confirm that the input -@{$tensors$Tensor dtype and shape} match the model. Moreover, if you +[Tensor dtype and shape](../guide/tensors.md) match the model. Moreover, if you want to test your model, you can use the CLI to do a sanity check by passing in sample inputs in various formats (for example, Python expressions) and then fetching the output. |