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diff --git a/tensorflow/docs_src/api_guides/python/input_dataset.md b/tensorflow/docs_src/api_guides/python/input_dataset.md deleted file mode 100644 index 911a76c2df..0000000000 --- a/tensorflow/docs_src/api_guides/python/input_dataset.md +++ /dev/null @@ -1,85 +0,0 @@ -# Dataset Input Pipeline -[TOC] - -`tf.data.Dataset` allows you to build complex input pipelines. See the -[Importing Data](../../guide/datasets.md) for an in-depth explanation of how to use this API. - -## Reader classes - -Classes that create a dataset from input files. - -* `tf.data.FixedLengthRecordDataset` -* `tf.data.TextLineDataset` -* `tf.data.TFRecordDataset` - -## Creating new datasets - -Static methods in `Dataset` that create new datasets. - -* `tf.data.Dataset.from_generator` -* `tf.data.Dataset.from_tensor_slices` -* `tf.data.Dataset.from_tensors` -* `tf.data.Dataset.list_files` -* `tf.data.Dataset.range` -* `tf.data.Dataset.zip` - -## Transformations on existing datasets - -These functions transform an existing dataset, and return a new dataset. Calls -can be chained together, as shown in the example below: - -``` -train_data = train_data.batch(100).shuffle().repeat() -``` - -* `tf.data.Dataset.apply` -* `tf.data.Dataset.batch` -* `tf.data.Dataset.cache` -* `tf.data.Dataset.concatenate` -* `tf.data.Dataset.filter` -* `tf.data.Dataset.flat_map` -* `tf.data.Dataset.interleave` -* `tf.data.Dataset.map` -* `tf.data.Dataset.padded_batch` -* `tf.data.Dataset.prefetch` -* `tf.data.Dataset.repeat` -* `tf.data.Dataset.shard` -* `tf.data.Dataset.shuffle` -* `tf.data.Dataset.skip` -* `tf.data.Dataset.take` - -### Custom transformation functions - -Custom transformation functions can be applied to a `Dataset` using `tf.data.Dataset.apply`. Below are custom transformation functions from `tf.contrib.data`: - -* `tf.contrib.data.batch_and_drop_remainder` -* `tf.contrib.data.dense_to_sparse_batch` -* `tf.contrib.data.enumerate_dataset` -* `tf.contrib.data.group_by_window` -* `tf.contrib.data.ignore_errors` -* `tf.contrib.data.map_and_batch` -* `tf.contrib.data.padded_batch_and_drop_remainder` -* `tf.contrib.data.parallel_interleave` -* `tf.contrib.data.rejection_resample` -* `tf.contrib.data.scan` -* `tf.contrib.data.shuffle_and_repeat` -* `tf.contrib.data.unbatch` - -## Iterating over datasets - -These functions make a `tf.data.Iterator` from a `Dataset`. - -* `tf.data.Dataset.make_initializable_iterator` -* `tf.data.Dataset.make_one_shot_iterator` - -The `Iterator` class also contains static methods that create a `tf.data.Iterator` that can be used with multiple `Dataset` objects. - -* `tf.data.Iterator.from_structure` -* `tf.data.Iterator.from_string_handle` - -## Extra functions from `tf.contrib.data` - -* `tf.contrib.data.get_single_element` -* `tf.contrib.data.make_saveable_from_iterator` -* `tf.contrib.data.read_batch_features` - |