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diff --git a/tensorflow/docs_src/api_guides/python/io_ops.md b/tensorflow/docs_src/api_guides/python/io_ops.md deleted file mode 100644 index d7ce6fdfde..0000000000 --- a/tensorflow/docs_src/api_guides/python/io_ops.md +++ /dev/null @@ -1,130 +0,0 @@ -# Inputs and Readers - -Note: Functions taking `Tensor` arguments can also take anything accepted by -`tf.convert_to_tensor`. - -[TOC] - -## Placeholders - -TensorFlow provides a placeholder operation that must be fed with data -on execution. For more info, see the section on [Feeding data](../../api_guides/python/reading_data.md#Feeding). - -* `tf.placeholder` -* `tf.placeholder_with_default` - -For feeding `SparseTensor`s which are composite type, -there is a convenience function: - -* `tf.sparse_placeholder` - -## Readers - -TensorFlow provides a set of Reader classes for reading data formats. -For more information on inputs and readers, see [Reading data](../../api_guides/python/reading_data.md). - -* `tf.ReaderBase` -* `tf.TextLineReader` -* `tf.WholeFileReader` -* `tf.IdentityReader` -* `tf.TFRecordReader` -* `tf.FixedLengthRecordReader` - -## Converting - -TensorFlow provides several operations that you can use to convert various data -formats into tensors. - -* `tf.decode_csv` -* `tf.decode_raw` - -- - - - -### Example protocol buffer - -TensorFlow's [recommended format for training examples](../../api_guides/python/reading_data.md#standard_tensorflow_format) -is serialized `Example` protocol buffers, [described -here](https://www.tensorflow.org/code/tensorflow/core/example/example.proto). -They contain `Features`, [described -here](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto). - -* `tf.VarLenFeature` -* `tf.FixedLenFeature` -* `tf.FixedLenSequenceFeature` -* `tf.SparseFeature` -* `tf.parse_example` -* `tf.parse_single_example` -* `tf.parse_tensor` -* `tf.decode_json_example` - -## Queues - -TensorFlow provides several implementations of 'Queues', which are -structures within the TensorFlow computation graph to stage pipelines -of tensors together. The following describe the basic Queue interface -and some implementations. To see an example use, see [Threading and Queues](../../api_guides/python/threading_and_queues.md). - -* `tf.QueueBase` -* `tf.FIFOQueue` -* `tf.PaddingFIFOQueue` -* `tf.RandomShuffleQueue` -* `tf.PriorityQueue` - -## Conditional Accumulators - -* `tf.ConditionalAccumulatorBase` -* `tf.ConditionalAccumulator` -* `tf.SparseConditionalAccumulator` - -## Dealing with the filesystem - -* `tf.matching_files` -* `tf.read_file` -* `tf.write_file` - -## Input pipeline - -TensorFlow functions for setting up an input-prefetching pipeline. -Please see the [reading data how-to](../../api_guides/python/reading_data.md) -for context. - -### Beginning of an input pipeline - -The "producer" functions add a queue to the graph and a corresponding -`QueueRunner` for running the subgraph that fills that queue. - -* `tf.train.match_filenames_once` -* `tf.train.limit_epochs` -* `tf.train.input_producer` -* `tf.train.range_input_producer` -* `tf.train.slice_input_producer` -* `tf.train.string_input_producer` - -### Batching at the end of an input pipeline - -These functions add a queue to the graph to assemble a batch of -examples, with possible shuffling. They also add a `QueueRunner` for -running the subgraph that fills that queue. - -Use `tf.train.batch` or `tf.train.batch_join` for batching -examples that have already been well shuffled. Use -`tf.train.shuffle_batch` or -`tf.train.shuffle_batch_join` for examples that would -benefit from additional shuffling. - -Use `tf.train.batch` or `tf.train.shuffle_batch` if you want a -single thread producing examples to batch, or if you have a -single subgraph producing examples but you want to run it in *N* threads -(where you increase *N* until it can keep the queue full). Use -`tf.train.batch_join` or `tf.train.shuffle_batch_join` -if you have *N* different subgraphs producing examples to batch and you -want them run by *N* threads. Use `maybe_*` to enqueue conditionally. - -* `tf.train.batch` -* `tf.train.maybe_batch` -* `tf.train.batch_join` -* `tf.train.maybe_batch_join` -* `tf.train.shuffle_batch` -* `tf.train.maybe_shuffle_batch` -* `tf.train.shuffle_batch_join` -* `tf.train.maybe_shuffle_batch_join` |