diff options
Diffstat (limited to 'tensorflow/docs_src/api_guides/python/reading_data.md')
| -rw-r--r-- | tensorflow/docs_src/api_guides/python/reading_data.md | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/tensorflow/docs_src/api_guides/python/reading_data.md b/tensorflow/docs_src/api_guides/python/reading_data.md index 78c36d965c..9f555ee85d 100644 --- a/tensorflow/docs_src/api_guides/python/reading_data.md +++ b/tensorflow/docs_src/api_guides/python/reading_data.md @@ -1,7 +1,7 @@ # Reading data Note: The preferred way to feed data into a tensorflow program is using the -@{$datasets$`tf.data` API}. +[`tf.data` API](../../guide/datasets.md). There are four methods of getting data into a TensorFlow program: @@ -16,7 +16,7 @@ There are four methods of getting data into a TensorFlow program: ## `tf.data` API -See the @{$guide/datasets} for an in-depth explanation of `tf.data.Dataset`. +See the [Importing Data](../../guide/datasets.md) for an in-depth explanation of `tf.data.Dataset`. The `tf.data` API enables you to extract and preprocess data from different input/file formats, and apply transformations such as batching, shuffling, and mapping functions over the dataset. This is an improved version @@ -56,8 +56,8 @@ in ## `QueueRunner` Warning: This section discusses implementing input pipelines using the -queue-based APIs which can be cleanly replaced by the @{$datasets$`tf.data` -API}. +queue-based APIs which can be cleanly replaced by the [`tf.data` +API](../../guide/datasets.md). A typical queue-based pipeline for reading records from files has the following stages: @@ -154,14 +154,14 @@ a uint8 tensor, standard operations can slice out each piece and reformat as needed. For CIFAR-10, you can see how to do the reading and decoding in [`tensorflow_models/tutorials/image/cifar10/cifar10_input.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_input.py) and described in -@{$deep_cnn#prepare-the-data$this tutorial}. +[this tutorial](../../tutorials/images/deep_cnn.md#prepare-the-data). #### Standard TensorFlow format Another approach is to convert whatever data you have into a supported format. This approach makes it easier to mix and match data sets and network architectures. The recommended format for TensorFlow is a -@{$python/python_io#tfrecords_format_details$TFRecords file} +[TFRecords file](../../api_guides/python/python_io.md#tfrecords_format_details) containing [`tf.train.Example` protocol buffers](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) (which contain @@ -279,7 +279,7 @@ This can be important: How many threads do you need? the `tf.train.shuffle_batch*` functions add a summary to the graph that indicates how full the example queue is. If you have enough reading threads, that summary will stay above zero. You can -@{$summaries_and_tensorboard$view your summaries as training progresses using TensorBoard}. +[view your summaries as training progresses using TensorBoard](../../guide/summaries_and_tensorboard.md). ### Creating threads to prefetch using `QueueRunner` objects @@ -368,7 +368,7 @@ threads got an error when running some operation (or an ordinary Python exception). For more about threading, queues, QueueRunners, and Coordinators -@{$threading_and_queues$see here}. +[see here](../../api_guides/python/threading_and_queues.md). #### Aside: How clean shut-down when limiting epochs works @@ -501,18 +501,18 @@ sessions, maybe in separate processes: model that reads validation input data. This is what is done `tf.estimator` and manually in -@{$deep_cnn#save-and-restore-checkpoints$the example CIFAR-10 model}. +[the example CIFAR-10 model](../../tutorials/images/deep_cnn.md#save-and-restore-checkpoints). This has a couple of benefits: * The eval is performed on a single snapshot of the trained variables. * You can perform the eval even after training has completed and exited. You can have the train and eval in the same graph in the same process, and share -their trained variables or layers. See @{$variables$the shared variables tutorial}. +their trained variables or layers. See [the shared variables tutorial](../../guide/variables.md). To support the single-graph approach -@{$guide/datasets$`tf.data`} also supplies -@{$guide/datasets#creating_an_iterator$advanced iterator types} that +[`tf.data`](../../guide/datasets.md) also supplies +[advanced iterator types](../../guide/datasets.md#creating_an_iterator) that that allow the user to change the input pipeline without rebuilding the graph or session. |