Inputs and Readers

tf.convert_to_tensor

[TOC]

Placeholders

Reading data

• tf.placeholder
• tf.placeholder_with_default

For feeding SparseTensors which are composite type,
there is a convenience function:

• tf.sparse_placeholder

Readers

Reading data

• 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

Reading data

• tf.VarLenFeature
• tf.FixedLenFeature
• tf.FixedLenSequenceFeature
• tf.SparseFeature
• tf.parse_example
• tf.parse_single_example
• tf.parse_tensor
• tf.decode_json_example

Queues

Threading and Queues

• 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

Reading data

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.

tf.train.shuffle_batch_join

tf.train.shuffle_batch_join

• 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