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Diffstat (limited to 'tensorflow/docs_src/guide/datasets.md')
| -rw-r--r-- | tensorflow/docs_src/guide/datasets.md | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/tensorflow/docs_src/guide/datasets.md b/tensorflow/docs_src/guide/datasets.md index 8b69860a68..bb18e8b79c 100644 --- a/tensorflow/docs_src/guide/datasets.md +++ b/tensorflow/docs_src/guide/datasets.md @@ -1,6 +1,6 @@ # Importing Data -The @{tf.data} API enables you to build complex input pipelines from +The `tf.data` API enables you to build complex input pipelines from simple, reusable pieces. For example, the pipeline for an image model might aggregate data from files in a distributed file system, apply random perturbations to each image, and merge randomly selected images into a batch @@ -51,7 +51,7 @@ Once you have a `Dataset` object, you can *transform* it into a new `Dataset` by chaining method calls on the `tf.data.Dataset` object. For example, you can apply per-element transformations such as `Dataset.map()` (to apply a function to each element), and multi-element transformations such as -`Dataset.batch()`. See the documentation for @{tf.data.Dataset} +`Dataset.batch()`. See the documentation for `tf.data.Dataset` for a complete list of transformations. The most common way to consume values from a `Dataset` is to make an @@ -211,13 +211,13 @@ for _ in range(20): sess.run(next_element) ``` -A **feedable** iterator can be used together with @{tf.placeholder} to select -what `Iterator` to use in each call to @{tf.Session.run}, via the familiar +A **feedable** iterator can be used together with `tf.placeholder` to select +what `Iterator` to use in each call to `tf.Session.run`, via the familiar `feed_dict` mechanism. It offers the same functionality as a reinitializable iterator, but it does not require you to initialize the iterator from the start of a dataset when you switch between iterators. For example, using the same training and validation example from above, you can use -@{tf.data.Iterator.from_string_handle} to define a feedable iterator +`tf.data.Iterator.from_string_handle` to define a feedable iterator that allows you to switch between the two datasets: ```python @@ -329,12 +329,12 @@ of an iterator will include all components in a single expression. ### Saving iterator state -The @{tf.contrib.data.make_saveable_from_iterator} function creates a +The `tf.contrib.data.make_saveable_from_iterator` function creates a `SaveableObject` from an iterator, which can be used to save and restore the current state of the iterator (and, effectively, the whole input -pipeline). A saveable object thus created can be added to @{tf.train.Saver} +pipeline). A saveable object thus created can be added to `tf.train.Saver` variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and -restoring in the same manner as a @{tf.Variable}. Refer to +restoring in the same manner as a `tf.Variable`. Refer to @{$saved_model$Saving and Restoring} for details on how to save and restore variables. @@ -488,7 +488,7 @@ dataset = dataset.flat_map( ### Consuming CSV data The CSV file format is a popular format for storing tabular data in plain text. -The @{tf.contrib.data.CsvDataset} class provides a way to extract records from +The `tf.contrib.data.CsvDataset` class provides a way to extract records from one or more CSV files that comply with [RFC 4180](https://tools.ietf.org/html/rfc4180). Given one or more filenames and a list of defaults, a `CsvDataset` will produce a tuple of elements whose types correspond to the types of the defaults @@ -757,9 +757,9 @@ dataset = dataset.repeat() ### Using high-level APIs -The @{tf.train.MonitoredTrainingSession} API simplifies many aspects of running +The `tf.train.MonitoredTrainingSession` API simplifies many aspects of running TensorFlow in a distributed setting. `MonitoredTrainingSession` uses the -@{tf.errors.OutOfRangeError} to signal that training has completed, so to use it +`tf.errors.OutOfRangeError` to signal that training has completed, so to use it with the `tf.data` API, we recommend using `Dataset.make_one_shot_iterator()`. For example: @@ -782,7 +782,7 @@ with tf.train.MonitoredTrainingSession(...) as sess: sess.run(training_op) ``` -To use a `Dataset` in the `input_fn` of a @{tf.estimator.Estimator}, we also +To use a `Dataset` in the `input_fn` of a `tf.estimator.Estimator`, we also recommend using `Dataset.make_one_shot_iterator()`. For example: ```python |