diff options
6 files changed, 15 insertions, 6 deletions
diff --git a/tensorflow/docs_src/api_guides/python/contrib.framework.md b/tensorflow/docs_src/api_guides/python/contrib.framework.md index f7f26e5626..6b4ce3a14d 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.framework.md +++ b/tensorflow/docs_src/api_guides/python/contrib.framework.md @@ -18,17 +18,20 @@ Framework utilities. * @{tf.contrib.framework.with_same_shape} ## Deprecation + * @{tf.contrib.framework.deprecated} * @{tf.contrib.framework.deprecated_args} * @{tf.contrib.framework.deprecated_arg_values} ## Arg_Scope + * @{tf.contrib.framework.arg_scope} * @{tf.contrib.framework.add_arg_scope} * @{tf.contrib.framework.has_arg_scope} * @{tf.contrib.framework.arg_scoped_arguments} ## Variables + * @{tf.contrib.framework.add_model_variable} * @{tf.train.assert_global_step} * @{tf.contrib.framework.assert_or_get_global_step} diff --git a/tensorflow/docs_src/api_guides/python/contrib.learn.md b/tensorflow/docs_src/api_guides/python/contrib.learn.md index 8b2fffa201..03838dc5ae 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.learn.md +++ b/tensorflow/docs_src/api_guides/python/contrib.learn.md @@ -25,6 +25,7 @@ Train and evaluate TensorFlow models. * @{tf.contrib.learn.LogisticRegressor} ## Distributed training utilities + * @{tf.contrib.learn.Experiment} * @{tf.contrib.learn.ExportStrategy} * @{tf.contrib.learn.TaskType} diff --git a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md index 496d43dfd7..143919fd84 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md +++ b/tensorflow/docs_src/api_guides/python/contrib.seq2seq.md @@ -21,6 +21,7 @@ wrapper. An instance of an `AttentionMechanism` is constructed with a ### Attention Mechanisms The two basic attention mechanisms are: + * @{tf.contrib.seq2seq.BahdanauAttention} (additive attention, [ref.](https://arxiv.org/abs/1409.0473)) * @{tf.contrib.seq2seq.LuongAttention} (multiplicative attention, @@ -118,14 +119,17 @@ outputs, _ = tf.contrib.seq2seq.dynamic_decode( ``` ### Decoder base class and functions + * @{tf.contrib.seq2seq.Decoder} * @{tf.contrib.seq2seq.dynamic_decode} ### Basic Decoder + * @{tf.contrib.seq2seq.BasicDecoderOutput} * @{tf.contrib.seq2seq.BasicDecoder} ### Decoder Helpers + * @{tf.contrib.seq2seq.Helper} * @{tf.contrib.seq2seq.CustomHelper} * @{tf.contrib.seq2seq.GreedyEmbeddingHelper} diff --git a/tensorflow/docs_src/api_guides/python/meta_graph.md b/tensorflow/docs_src/api_guides/python/meta_graph.md index 0eff900093..f1c3adc22c 100644 --- a/tensorflow/docs_src/api_guides/python/meta_graph.md +++ b/tensorflow/docs_src/api_guides/python/meta_graph.md @@ -22,14 +22,14 @@ protocol buffer. It contains the following fields: * [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) for describing the graph. * [`SaverDef`](https://www.tensorflow.org/code/tensorflow/core/protobuf/saver.proto) for the saver. * [`CollectionDef`](https://www.tensorflow.org/code/tensorflow/core/protobuf/meta_graph.proto) -map that further describes additional components of the model, such as +map that further describes additional components of the model such as @{$python/state_ops$`Variables`}, -@{tf.train.QueueRunner}, etc. In order for a Python object to be serialized +@{tf.train.QueueRunner}, etc. + +In order for a Python object to be serialized to and from `MetaGraphDef`, the Python class must implement `to_proto()` and `from_proto()` methods, and register them with the system using -`register_proto_function`. - - For example, +`register_proto_function`. For example: ```Python def to_proto(self, export_scope=None): diff --git a/tensorflow/docs_src/api_guides/python/train.md b/tensorflow/docs_src/api_guides/python/train.md index 80fe9784de..cbc5052946 100644 --- a/tensorflow/docs_src/api_guides/python/train.md +++ b/tensorflow/docs_src/api_guides/python/train.md @@ -54,6 +54,7 @@ gradients. * @{tf.global_norm} ## Decaying the learning rate + * @{tf.train.exponential_decay} * @{tf.train.inverse_time_decay} * @{tf.train.natural_exp_decay} diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index b1796cf9b2..cb0f5ca924 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -78,7 +78,7 @@ training CIFAR-10 illustrates the use of the `tf.data` API along with The `tf.data` API utilizes C++ multi-threading and has a much lower overhead than the Python-based `queue_runner` that is limited by Python's multi-threading performance. A detailed performance guide for the `tf.data` API can be found -[here](@{$datasets_performance}). +@{$datasets_performance$here}. While feeding data using a `feed_dict` offers a high level of flexibility, in general `feed_dict` does not provide a scalable solution. If only a single GPU |