path: root/tensorflow/g3doc/api_docs/python/io_ops.md

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# Inputs and Readers
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## Contents
* [Placeholders](#AUTOGENERATED-placeholders)
  * [tf.placeholder(dtype, shape=None, name=None)](#placeholder)
* [Readers](#AUTOGENERATED-readers)
  * [class tf.ReaderBase](#ReaderBase)
  * [class tf.TextLineReader](#TextLineReader)
  * [class tf.WholeFileReader](#WholeFileReader)
  * [class tf.IdentityReader](#IdentityReader)
  * [class tf.TFRecordReader](#TFRecordReader)
  * [class tf.FixedLengthRecordReader](#FixedLengthRecordReader)
* [Converting](#AUTOGENERATED-converting)
  * [tf.decode_csv(records, record_defaults, field_delim=None, name=None)](#decode_csv)
  * [tf.decode_raw(bytes, out_type, little_endian=None, name=None)](#decode_raw)
  * [tf.parse_example(serialized, names=None, sparse_keys=None, sparse_types=None, dense_keys=None, dense_types=None, dense_defaults=None, dense_shapes=None, name='ParseExample')](#parse_example)
  * [tf.parse_single_example(serialized, names=None, sparse_keys=None, sparse_types=None, dense_keys=None, dense_types=None, dense_defaults=None, dense_shapes=None, name='ParseSingleExample')](#parse_single_example)
* [Queues](#AUTOGENERATED-queues)
  * [class tf.QueueBase](#QueueBase)
  * [class tf.FIFOQueue](#FIFOQueue)
  * [class tf.RandomShuffleQueue](#RandomShuffleQueue)
* [Dealing with the filesystem](#AUTOGENERATED-dealing-with-the-filesystem)
  * [tf.matching_files(pattern, name=None)](#matching_files)
  * [tf.read_file(filename, name=None)](#read_file)
* [Input pipeline](#AUTOGENERATED-input-pipeline)
  * [Beginning of an input pipeline](#AUTOGENERATED-beginning-of-an-input-pipeline)
  * [tf.train.match_filenames_once(pattern, name=None)](#match_filenames_once)
  * [tf.train.limit_epochs(tensor, num_epochs=None, name=None)](#limit_epochs)
  * [tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None)](#range_input_producer)
  * [tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None)](#slice_input_producer)
  * [tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None)](#string_input_producer)
  * [Batching at the end of an input pipeline](#AUTOGENERATED-batching-at-the-end-of-an-input-pipeline)
  * [tf.train.batch(tensor_list, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, name=None)](#batch)
  * [tf.train.batch_join(tensor_list_list, batch_size, capacity=32, enqueue_many=False, shapes=None, name=None)](#batch_join)
  * [tf.train.shuffle_batch(tensor_list, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, name=None)](#shuffle_batch)
  * [tf.train.shuffle_batch_join(tensor_list_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, name=None)](#shuffle_batch_join)


<!-- TOC-END This section was generated by neural network, THANKS FOR READING! -->

## Placeholders <div class="md-anchor" id="AUTOGENERATED-placeholders">{#AUTOGENERATED-placeholders}</div>

TensorFlow provides a placeholder operation that must be fed with data
on execution.  For more info, see the section on [Feeding
data](../../how_tos/reading_data/index.md#feeding).

- - -

### tf.placeholder(dtype, shape=None, name=None) <div class="md-anchor" id="placeholder">{#placeholder}</div>

Inserts a placeholder for a tensor that will be always fed.

**Important**: This tensor will produce an error if evaluated. Its value must
be fed using the `feed_dict` optional argument to `Session.run()`,
`Tensor.eval()`, or `Operation.run()`.

For example:

```python
x = tf.placeholder(float, shape=(1024, 1024))
y = tf.matmul(x, x)

with tf.Session() as sess:
  print sess.run(y)  # ERROR: will fail because x was not fed.

  rand_array = np.random.rand(1024, 1024)
  print sess.run(y, feed_dict={x: rand_array})  # Will succeed.
```

##### Args:


*  <b>dtype</b>: The type of elements in the tensor to be fed.
*  <b>shape</b>: The shape of the tensor to be fed (optional). If the shape is not
    specified, you can feed a tensor of any shape.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` that may be used as a handle for feeding a value, but not
  evaluated directly.



## Readers <div class="md-anchor" id="AUTOGENERATED-readers">{#AUTOGENERATED-readers}</div>

TensorFlow provides a set of Reader classes for reading data formats.
For more information on inputs and readers, see [Reading
data](../../how_tos/reading_data/index.md).

- - -

### class tf.ReaderBase <div class="md-anchor" id="ReaderBase">{#ReaderBase}</div>

Base class for different Reader types, that produce a record every step.

Conceptually, Readers convert string 'work units' into records (key,
value pairs).  Typically the 'work units' are filenames and the
records are extracted from the contents of those files.  We want a
single record produced per step, but a work unit can correspond to
many records.

Therefore we introduce some decoupling using a queue.  The queue
contains the work units and the Reader dequeues from the queue when
it is asked to produce a record (via Read()) but it has finished the
last work unit.
- - -

#### tf.ReaderBase.__init__(reader_ref, supports_serialize=False) {#ReaderBase.__init__}

Creates a new ReaderBase.

##### Args:


*  <b>reader_ref</b>: The operation that implements the reader.
*  <b>supports_serialize</b>: True if the reader implementation can
    serialize its state.


- - -

#### tf.ReaderBase.num_records_produced(name=None) {#ReaderBase.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.ReaderBase.num_work_units_completed(name=None) {#ReaderBase.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.ReaderBase.read(queue, name=None) {#ReaderBase.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.ReaderBase.reader_ref {#ReaderBase.reader_ref}

Op that implements the reader.

- - -

#### tf.ReaderBase.reset(name=None) {#ReaderBase.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.ReaderBase.restore_state(state, name=None) {#ReaderBase.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.ReaderBase.serialize_state(name=None) {#ReaderBase.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.ReaderBase.supports_serialize {#ReaderBase.supports_serialize}

Whether the Reader implementation can serialize its state.


- - -

### class tf.TextLineReader <div class="md-anchor" id="TextLineReader">{#TextLineReader}</div>

A Reader that outputs the lines of a file delimited by newlines.

Newlines are stripped from the output.
See ReaderBase for supported methods.
- - -

#### tf.TextLineReader.__init__(skip_header_lines=None, name=None) {#TextLineReader.__init__}

Create a TextLineReader.

##### Args:


*  <b>skip_header_lines</b>: An optional int. Defaults to 0.  Number of lines
    to skip from the beginning of every file.
*  <b>name</b>: A name for the operation (optional).


- - -

#### tf.TextLineReader.num_records_produced(name=None) {#TextLineReader.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.TextLineReader.num_work_units_completed(name=None) {#TextLineReader.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.TextLineReader.read(queue, name=None) {#TextLineReader.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.TextLineReader.reader_ref {#TextLineReader.reader_ref}

Op that implements the reader.

- - -

#### tf.TextLineReader.reset(name=None) {#TextLineReader.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.TextLineReader.restore_state(state, name=None) {#TextLineReader.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.TextLineReader.serialize_state(name=None) {#TextLineReader.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.TextLineReader.supports_serialize {#TextLineReader.supports_serialize}

Whether the Reader implementation can serialize its state.


- - -

### class tf.WholeFileReader <div class="md-anchor" id="WholeFileReader">{#WholeFileReader}</div>

A Reader that outputs the entire contents of a file as a value.

To use, enqueue filenames in a Queue.  The output of Read will
be a filename (key) and the contents of that file (value).

See ReaderBase for supported methods.
- - -

#### tf.WholeFileReader.__init__(name=None) {#WholeFileReader.__init__}

Create a WholeFileReader.

##### Args:


*  <b>name</b>: A name for the operation (optional).


- - -

#### tf.WholeFileReader.num_records_produced(name=None) {#WholeFileReader.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.WholeFileReader.num_work_units_completed(name=None) {#WholeFileReader.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.WholeFileReader.read(queue, name=None) {#WholeFileReader.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.WholeFileReader.reader_ref {#WholeFileReader.reader_ref}

Op that implements the reader.

- - -

#### tf.WholeFileReader.reset(name=None) {#WholeFileReader.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.WholeFileReader.restore_state(state, name=None) {#WholeFileReader.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.WholeFileReader.serialize_state(name=None) {#WholeFileReader.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.WholeFileReader.supports_serialize {#WholeFileReader.supports_serialize}

Whether the Reader implementation can serialize its state.


- - -

### class tf.IdentityReader <div class="md-anchor" id="IdentityReader">{#IdentityReader}</div>

A Reader that outputs the queued work as both the key and value.

To use, enqueue strings in a Queue.  Read will take the front
work string and output (work, work).

See ReaderBase for supported methods.
- - -

#### tf.IdentityReader.__init__(name=None) {#IdentityReader.__init__}

Create a IdentityReader.

##### Args:


*  <b>name</b>: A name for the operation (optional).


- - -

#### tf.IdentityReader.num_records_produced(name=None) {#IdentityReader.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.IdentityReader.num_work_units_completed(name=None) {#IdentityReader.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.IdentityReader.read(queue, name=None) {#IdentityReader.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.IdentityReader.reader_ref {#IdentityReader.reader_ref}

Op that implements the reader.

- - -

#### tf.IdentityReader.reset(name=None) {#IdentityReader.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.IdentityReader.restore_state(state, name=None) {#IdentityReader.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.IdentityReader.serialize_state(name=None) {#IdentityReader.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.IdentityReader.supports_serialize {#IdentityReader.supports_serialize}

Whether the Reader implementation can serialize its state.


- - -

### class tf.TFRecordReader <div class="md-anchor" id="TFRecordReader">{#TFRecordReader}</div>

A Reader that outputs the records from a TFRecords file.

See ReaderBase for supported methods.
- - -

#### tf.TFRecordReader.__init__(name=None) {#TFRecordReader.__init__}

Create a TFRecordReader.

##### Args:


*  <b>name</b>: A name for the operation (optional).


- - -

#### tf.TFRecordReader.num_records_produced(name=None) {#TFRecordReader.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.TFRecordReader.num_work_units_completed(name=None) {#TFRecordReader.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.TFRecordReader.read(queue, name=None) {#TFRecordReader.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.TFRecordReader.reader_ref {#TFRecordReader.reader_ref}

Op that implements the reader.

- - -

#### tf.TFRecordReader.reset(name=None) {#TFRecordReader.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.TFRecordReader.restore_state(state, name=None) {#TFRecordReader.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.TFRecordReader.serialize_state(name=None) {#TFRecordReader.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.TFRecordReader.supports_serialize {#TFRecordReader.supports_serialize}

Whether the Reader implementation can serialize its state.


- - -

### class tf.FixedLengthRecordReader <div class="md-anchor" id="FixedLengthRecordReader">{#FixedLengthRecordReader}</div>

A Reader that outputs fixed-length records from a file.

See ReaderBase for supported methods.
- - -

#### tf.FixedLengthRecordReader.__init__(record_bytes, header_bytes=None, footer_bytes=None, name=None) {#FixedLengthRecordReader.__init__}

Create a FixedLengthRecordReader.

##### Args:


*  <b>record_bytes</b>: An int.
*  <b>header_bytes</b>: An optional int. Defaults to 0.
*  <b>footer_bytes</b>: An optional int. Defaults to 0.
*  <b>name</b>: A name for the operation (optional).


- - -

#### tf.FixedLengthRecordReader.num_records_produced(name=None) {#FixedLengthRecordReader.num_records_produced}

Returns the number of records this reader has produced.

This is the same as the number of Read executions that have
succeeded.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.FixedLengthRecordReader.num_work_units_completed(name=None) {#FixedLengthRecordReader.num_work_units_completed}

Returns the number of work units this reader has finished processing.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  An int64 Tensor.


- - -

#### tf.FixedLengthRecordReader.read(queue, name=None) {#FixedLengthRecordReader.read}

Returns the next record (key, value pair) produced by a reader.

Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).

##### Args:


*  <b>queue</b>: A Queue or a mutable string Tensor representing a handle
    to a Queue, with string work items.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of Tensors (key, value).

*  <b>key</b>: A string scalar Tensor.
*  <b>value</b>: A string scalar Tensor.


- - -

#### tf.FixedLengthRecordReader.reader_ref {#FixedLengthRecordReader.reader_ref}

Op that implements the reader.

- - -

#### tf.FixedLengthRecordReader.reset(name=None) {#FixedLengthRecordReader.reset}

Restore a reader to its initial clean state.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.FixedLengthRecordReader.restore_state(state, name=None) {#FixedLengthRecordReader.restore_state}

Restore a reader to a previously saved state.

Not all Readers support being restored, so this can produce an
Unimplemented error.

##### Args:


*  <b>state</b>: A string Tensor.
    Result of a SerializeState of a Reader with matching type.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The created Operation.


- - -

#### tf.FixedLengthRecordReader.serialize_state(name=None) {#FixedLengthRecordReader.serialize_state}

Produce a string tensor that encodes the state of a reader.

Not all Readers support being serialized, so this can produce an
Unimplemented error.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A string Tensor.


- - -

#### tf.FixedLengthRecordReader.supports_serialize {#FixedLengthRecordReader.supports_serialize}

Whether the Reader implementation can serialize its state.



## Converting <div class="md-anchor" id="AUTOGENERATED-converting">{#AUTOGENERATED-converting}</div>

TensorFlow provides several operations that you can use to convert various data
formats into tensors.

- - -

### tf.decode_csv(records, record_defaults, field_delim=None, name=None) <div class="md-anchor" id="decode_csv">{#decode_csv}</div>

Convert CSV records to tensors. Each column maps to one tensor.

RFC 4180 format is expected for the CSV records.
(https://tools.ietf.org/html/rfc4180)
Note that we allow leading and trailing spaces with int or float field.

##### Args:


*  <b>records</b>: A `Tensor` of type `string`.
    Each string is a record/row in the csv and all records should have
    the same format.
*  <b>record_defaults</b>: A list of `Tensor` objects with types from: `float32`, `int32`, `int64`, `string`.
    One tensor per column of the input record, with either a
    scalar default value for that column or empty if the column is required.
*  <b>field_delim</b>: An optional `string`. Defaults to `","`.
    delimiter to separate fields in a record.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A list of `Tensor` objects. Has the same type as `record_defaults`.
  Each tensor will have the same shape as records.


- - -

### tf.decode_raw(bytes, out_type, little_endian=None, name=None) <div class="md-anchor" id="decode_raw">{#decode_raw}</div>

Reinterpret the bytes of a string as a vector of numbers.

##### Args:


*  <b>bytes</b>: A `Tensor` of type `string`.
    All the elements must have the same length.
*  <b>out_type</b>: A `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64`.
*  <b>little_endian</b>: An optional `bool`. Defaults to `True`.
    Whether the input bytes are in little-endian order.
    Ignored for out_types that are stored in a single byte like uint8.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of type `out_type`.
  A Tensor with one more dimension than the input bytes.  The
  added dimension will have size equal to the length of the elements
  of bytes divided by the number of bytes to represent out_type.


- - -

### tf.parse_example(serialized, names=None, sparse_keys=None, sparse_types=None, dense_keys=None, dense_types=None, dense_defaults=None, dense_shapes=None, name='ParseExample') <div class="md-anchor" id="parse_example">{#parse_example}</div>

Parse Example protos.

##### Args:


*  <b>serialized</b>: string vector, a batch of binary serialized Example protos.
*  <b>names</b>: A string vector, the names of the serialized protos.
    "names" may contain, e.g., table key (descriptive) names for the
    corresponding serialized protos.  These are purely useful for debugging
    purposes, and the presence of values here has no effect on the output.
    "names" may be an empty vector, if no names are available.
    If non-empty, this vector must be the same length as "serialized".
*  <b>sparse_keys</b>: A string list of keys in the Examples' features.
    These keys are associated with sparse values.
*  <b>sparse_types</b>: A list of DTypes.
    This list's length must match that of sparse_keys.  Currently
    parse_example supports tf.float32 (FloatList), tf.int64 (Int64List),
    and tf.string (BytesList).
*  <b>dense_keys</b>: A string list of keys in the Examples' features.
    These keys are associated with dense values.
*  <b>dense_types</b>: A list of DTypes.
    This list's length must match that of dense_keys.  Currently
    parse_example supports tf.float32 (FloatList), tf.int64 (Int64List),
    and tf.string (BytesList).
*  <b>dense_defaults</b>: A dict of {key:Tensor} (some may be missing).
    The keys of the dict must match the dense_keys of the feature.
    If a key is not present in this dictionary, the corresponding dense
    Feature is required in all elements of serialized.
*  <b>dense_shapes</b>: A list of tuples.
    Entries provide the shape of data in each dense Feature in features.
    The length of dense_shapes must be the same as the length of dense_keys.
    The number of elements in the Feature corresponding to dense_key[j]
    must always have np.prod(dense_shapes[j]) entries.
    If dense_shapes[j] == (D0, D1, ..., DN) then the the shape of output
    Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
    The dense outputs are just the inputs row-stacked by batch.
*  <b>name</b>: (Optional) Name of Op in the graph.

##### Returns:

  A dictionary mapping keys to Tensors and SparseTensors.

  The key dense_keys[j] is mapped to a tensor of type dense_types[j] and
  of shape (serialized.size(),) + dense_shapes[j] (i.e., the dense outputs are
  inputs, reshaped in row-major format and then row-stacked by batch).

  The key sparse_keys[j] is mapped to a SparseTensor of type sparse_types[j].
  The SparseTensor represents a ragged matrix.  Its indices are [batch, index]
  where "batch" is is the batch entry the value is from, and "index" is the
  value's index in the list of values associated with that feature
  and example.  For example, if one expects a tf.float32 sparse feature "ft"
  and three serialized examples are provided:

  serialized = [

*  <b>features</b>: 
      { feature: [ key: { "ft" value: float_list: { value: [1.0, 2.0] } } ] },
*  <b>features</b>: 
      { feature: [] },
*  <b>features</b>: 
      { feature: [ key: { "ft" value: float_list: { value: [3.0] } } ] }
  ]

  then the output will look like:

    {"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]],
                        values=[1.0, 2.0, 3.0],
                        shape=(3, 2)) }

##### Raises:


*  <b>ValueError</b>: If sparse and dense keys intersect, or input lengths do not
    match up for sparse_* (similarly for dense_*).
*  <b>TypeError</b>: If an input is malformed.

Example input, format, and output: Just Sparse Inputs
================================================

Given two brain.Example input protos:


*  <b>serialized</b>: // serialized versions of the protos below
  [features: {

*  <b>feature</b>: { key: "kw" value: { bytes_list: { value: [ "knit", "big" ] } } }
*  <b>feature</b>: { key: "gps" value: { float_list: { value: [] } } }
   },
*  <b>features</b>: {
*  <b>feature</b>: { key: "kw" value: { bytes_list: { value: [ "emmy" ] } } }
*  <b>feature</b>: { key: "dank" value: { int64_list: { value: [ 42 ] } } }
*  <b>feature</b>: { key: "gps" value: { } }
  }]

*  <b>names</b>: ["input0", "input1"],
*  <b>sparse_keys</b>: ["kw", "dank", "gps"]
*  <b>sparse_types</b>: [DT_STRING, DT_INT64, DT_FLOAT]

Then the expected output is a dictionary:
{
  "kw": SparseTensor(
      indices=[[0, 0], [0, 1], [1, 0]],
      values=["knit", "big", "emmy"]
      shape=[2, 2]),
  "dank": SparseTensor(
      indices=[[1, 0]],
      values=[42],
      shape=[2, 1]),
  "gps": SparseTensor(
      indices=[],
      values=[],
      shape=[2, 0]),
}


Example input, format, and output: Dense Inputs (without defaults)
==================================================================

Given two brain.Example input protos:


*  <b>serialized</b>: // serialized versions of the protos below
  [features: {

*  <b>feature</b>: { key: "age" value: { int64_list: { value: [ 0 ] } } }
*  <b>feature</b>: { key: "gender" value: { bytes_list: { value: [ "f" ] } } }
   },
*  <b>features</b>: {
*  <b>feature</b>: { key: "age" value: { int64_list: { value: [] } } }
*  <b>feature</b>: { key: "gender" value: { bytes_list: { value: [ "f" ] } } }
  }]

*  <b>names</b>: ["input0", "input1"],
*  <b>dense_keys</b>: np.array(["age", "gender"])
*  <b>dense_types</b>: [tf.int64, tf.string]
*  <b>dense_defaults</b>: {
  "age": -1  # defaults to -1 if missing
             # "gender" has no specified default so it's required
}

*  <b>dense_shapes</b>: [(1,), (1,)]  # age, gender, label, weight

Then the expected output is a dictionary:
{
  "age": [[0], [-1]],
  "gender": [["f"], ["f"]],
}


Example input, format, and output: Dense Inputs (with defaults)
===============================================================

Given two brain.Example input protos:


*  <b>serialized</b>: // serialized versions of the protos below
  [features: {

*  <b>feature</b>: { key: "weight" value: { float_list: { value: [ 1.0 ] } } }
   },
*  <b>features</b>: {
*  <b>feature</b>: { key: "label" value: { float_list: { value: [ -1.0, 0.0 ] } } }
  }]

*  <b>names</b>: ["input0", "input1"],
*  <b>dense_keys</b>: np.array(["label", "weight"])
*  <b>dense_defaults</b>: {
  "label": [1.0, 2.0],  # float (default: vector)
  "weight": 5.0         # float (default: scalar, 5.0)
}

*  <b>dense_shapes</b>: [(2,), (1,)]  # age, gender, label, weight

Then the expected output is a dictionary:
{
  "label": [[1.0, 2.0], [-1.0, 0.0]],
  "weight": [[1.0], [5.0]],
}


- - -

### tf.parse_single_example(serialized, names=None, sparse_keys=None, sparse_types=None, dense_keys=None, dense_types=None, dense_defaults=None, dense_shapes=None, name='ParseSingleExample') <div class="md-anchor" id="parse_single_example">{#parse_single_example}</div>

Identical to parse_example but for scalar serialized and names.

##### Args:


*  <b>serialized</b>: A scalar string, a single serialized Example.
    See parse_example documentation for more details.
*  <b>names</b>: (Optional) A scalar string, the associated name.
    See parse_example documentation for more details.
*  <b>sparse_keys</b>: See parse_example documentation for more details.
*  <b>sparse_types</b>: See parse_example documentation for more details.
*  <b>dense_keys</b>: See parse_example documentation for more details.
*  <b>dense_types</b>: See parse_example documentation for more details.
*  <b>dense_defaults</b>: See parse_example documentation for more details.
*  <b>dense_shapes</b>: See parse_example documentation for more details.
*  <b>name</b>: Optional op name.

##### Returns:

  A dictionary mapping keys to Tensors and SparseTensors.

  For dense tensors, the Tensor is identical to the output of parse_example,
  except it is one less dimension (the first, batch, dimension is removed).

  For SparseTensors:
    The first (batch) column of the indices matrix is removed
      (it is now a column vector).
    The values vector is unchanged.
    The first (batch_size) entry of the shape vector is removed
      (it is now a single element vector).

##### Raises:


*  <b>ValueError</b>: if "scalar" or "names" have known shapes, and are not scalars.



## Queues <div class="md-anchor" id="AUTOGENERATED-queues">{#AUTOGENERATED-queues}</div>

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](../../how_tos/threading_and_queues/index.md).

- - -

### class tf.QueueBase <div class="md-anchor" id="QueueBase">{#QueueBase}</div>

Base class for queue implementations.

A queue is a TensorFlow data structure that stores tensors across
multiple steps, and exposes operations that enqueue and dequeue
tensors.

Each queue element is a tuple of one or more tensors, where each
tuple component has a static dtype, and may have a static shape. The
queue implementations support versions of enqueue and dequeue that
handle single elements, versions that support enqueuing and
dequeuing a batch of elements at once.

See [`tf.FIFOQueue`](#FIFOQueue) and
[`tf.RandomShuffleQueue`](#RandomShuffleQueue) for concrete
implementations of this class, and instructions on how to create
them.

- - -

#### tf.QueueBase.enqueue(vals, name=None) {#QueueBase.enqueue}

Enqueues one element to this queue.

If the queue is full when this operation executes, it will block
until the element has been enqueued.

##### Args:


*  <b>vals</b>: The tuple of `Tensor` objects to be enqueued.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The operation that enqueues a new tuple of tensors to the queue.


- - -

#### tf.QueueBase.enqueue_many(vals, name=None) {#QueueBase.enqueue_many}

Enqueues zero or elements to this queue.

This operation slices each component tensor along the 0th dimension to
make multiple queue elements. All of the tensors in `vals` must have the
same size in the 0th dimension.

If the queue is full when this operation executes, it will block
until all of the elements have been enqueued.

##### Args:


*  <b>vals</b>: The tensor or tuple of tensors from which the queue elements
    are taken.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The operation that enqueues a batch of tuples of tensors to the queue.



- - -

#### tf.QueueBase.dequeue(name=None) {#QueueBase.dequeue}

Dequeues one element from this queue.

If the queue is empty when this operation executes, it will block
until there is an element to dequeue.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The tuple of tensors that was dequeued.


- - -

#### tf.QueueBase.dequeue_many(n, name=None) {#QueueBase.dequeue_many}

Dequeues and concatenates `n` elements from this queue.

This operation concatenates queue-element component tensors along
the 0th dimension to make a single component tensor.  All of the
components in the dequeued tuple will have size `n` in the 0th dimension.

If the queue contains fewer than `n` elements when this operation
executes, it will block until `n` elements have been dequeued.

##### Args:


*  <b>n</b>: A scalar `Tensor` containing the number of elements to dequeue.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The tuple of concatenated tensors that was dequeued.



- - -

#### tf.QueueBase.size(name=None) {#QueueBase.size}

Compute the number of elements in this queue.

##### Args:


*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A scalar tensor containing the number of elements in this queue.



- - -

#### tf.QueueBase.close(cancel_pending_enqueues=False, name=None) {#QueueBase.close}

Closes this queue.

This operation signals that no more elements will be enqueued in
the given queue. Subsequent `enqueue` and `enqueue_many`
operations will fail. Subsequent `dequeue` and `dequeue_many`
operations will continue to succeed if sufficient elements remain
in the queue. Subsequent `dequeue` and `dequeue_many` operations
that would block will fail immediately.

If `cancel_pending_enqueues` is `True`, all pending requests will also
be cancelled.

##### Args:


*  <b>cancel_pending_enqueues</b>: (Optional.) A boolean, defaulting to
    `False` (described above).
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  The operation that closes the queue.



#### Other Methods
- - -

#### tf.QueueBase.__init__(dtypes, shapes, queue_ref) {#QueueBase.__init__}

Constructs a queue object from a queue reference.

##### Args:


*  <b>dtypes</b>: A list of types.  The length of dtypes must equal the number
    of tensors in each element.
*  <b>shapes</b>: Constraints on the shapes of tensors in an element:
    A list of shape tuples or None. This list is the same length
    as dtypes.  If the shape of any tensors in the element are constrained,
    all must be; shapes can be None if the shapes should not be constrained.
*  <b>queue_ref</b>: The queue reference, i.e. the output of the queue op.


- - -

#### tf.QueueBase.dtypes {#QueueBase.dtypes}

The list of dtypes for each component of a queue element.

- - -

#### tf.QueueBase.name {#QueueBase.name}

The name of the underlying queue.

- - -

#### tf.QueueBase.queue_ref {#QueueBase.queue_ref}

The underlying queue reference.


- - -

### class tf.FIFOQueue <div class="md-anchor" id="FIFOQueue">{#FIFOQueue}</div>

A queue implementation that dequeues elements in first-in-first out order.

See [`tf.QueueBase`](#QueueBase) for a description of the methods on
this class.

- - -

#### tf.FIFOQueue.__init__(capacity, dtypes, shapes=None, shared_name=None, name='fifo_queue') {#FIFOQueue.__init__}

Creates a queue that dequeues elements in a first-in first-out order.

A `FIFOQueue` has bounded capacity; supports multiple concurrent
producers and consumers; and provides exactly-once delivery.

A `FIFOQueue` holds a list of up to `capacity` elements. Each
element is a fixed-length tuple of tensors whose dtypes are
described by `dtypes`, and whose shapes are optionally described
by the `shapes` argument.

If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.

##### Args:


*  <b>capacity</b>: An integer. The upper bound on the number of elements
    that may be stored in this queue.
*  <b>dtypes</b>: A list of `DType` objects. The length of `dtypes` must equal
    the number of tensors in each queue element.
*  <b>shapes</b>: (Optional.) A list of fully-defined `TensorShape` objects,
    with the same length as `dtypes` or `None`.
*  <b>shared_name</b>: (Optional.) If non-empty, this queue will be shared under
    the given name across multiple sessions.
*  <b>name</b>: Optional name for the queue operation.



- - -

### class tf.RandomShuffleQueue <div class="md-anchor" id="RandomShuffleQueue">{#RandomShuffleQueue}</div>

A queue implementation that dequeues elements in a random order.

See [`tf.QueueBase`](#QueueBase) for a description of the methods on
this class.

- - -

#### tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, seed=None, shared_name=None, name='random_shuffle_queue') {#RandomShuffleQueue.__init__}

Create a queue that dequeues elements in a random order.

A `RandomShuffleQueue` has bounded capacity; supports multiple
concurrent producers and consumers; and provides exactly-once
delivery.

A `RandomShuffleQueue` holds a list of up to `capacity`
elements. Each element is a fixed-length tuple of tensors whose
dtypes are described by `dtypes`, and whose shapes are optionally
described by the `shapes` argument.

If the `shapes` argument is specified, each component of a queue
element must have the respective fixed shape. If it is
unspecified, different queue elements may have different shapes,
but the use of `dequeue_many` is disallowed.

The `min_after_dequeue` argument allows the caller to specify a
minimum number of elements that will remain in the queue after a
`dequeue` or `dequeue_many` operation completes, to ensure a
minimum level of mixing of elements. This invariant is maintained
by blocking those operations until sufficient elements have been
enqueued. The `min_after_dequeue` argument is ignored after the
queue has been closed.

##### Args:


*  <b>capacity</b>: An integer. The upper bound on the number of elements
    that may be stored in this queue.
*  <b>min_after_dequeue</b>: An integer (described above).
*  <b>dtypes</b>: A list of `DType` objects. The length of `dtypes` must equal
    the number of tensors in each queue element.
*  <b>shapes</b>: (Optional.) A list of fully-defined `TensorShape` objects,
    with the same length as `dtypes` or `None`.
*  <b>seed</b>: A Python integer. Used to create a random seed.
    See [`set_random_seed`](constant_op.md#set_random_seed) for behavior.
*  <b>shared_name</b>: (Optional.) If non-empty, this queue will be shared under
    the given name across multiple sessions.
*  <b>name</b>: Optional name for the queue operation.




## Dealing with the filesystem <div class="md-anchor" id="AUTOGENERATED-dealing-with-the-filesystem">{#AUTOGENERATED-dealing-with-the-filesystem}</div>

- - -

### tf.matching_files(pattern, name=None) <div class="md-anchor" id="matching_files">{#matching_files}</div>

Returns the set of files matching a pattern.

Note that this routine only supports wildcard characters in the
basename portion of the pattern, not in the directory portion.

##### Args:


*  <b>pattern</b>: A `Tensor` of type `string`. A (scalar) shell wildcard pattern.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of type `string`. A vector of matching filenames.


- - -

### tf.read_file(filename, name=None) <div class="md-anchor" id="read_file">{#read_file}</div>

Reads and outputs the entire contents of the input filename.

##### Args:


*  <b>filename</b>: A `Tensor` of type `string`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of type `string`.



## Input pipeline <div class="md-anchor" id="AUTOGENERATED-input-pipeline">{#AUTOGENERATED-input-pipeline}</div>

TensorFlow functions for setting up an input-prefetching pipeline.
Please see the [reading data how-to](../../how_tos/reading_data.md)
for context.

### Beginning of an input pipeline <div class="md-anchor" id="AUTOGENERATED-beginning-of-an-input-pipeline">{#AUTOGENERATED-beginning-of-an-input-pipeline}</div>

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(pattern, name=None) <div class="md-anchor" id="match_filenames_once">{#match_filenames_once}</div>

Save the list of files matching pattern, so it is only computed once.

##### Args:


*  <b>pattern</b>: A file pattern (glob).
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A variable that is initialized to the list of files matching pattern.


- - -

### tf.train.limit_epochs(tensor, num_epochs=None, name=None) <div class="md-anchor" id="limit_epochs">{#limit_epochs}</div>

Returns tensor num_epochs times and then raises an OutOfRange error.

##### Args:


*  <b>tensor</b>: Any Tensor.
*  <b>num_epochs</b>: An integer (optional).  If specified, limits the number
    of steps the output tensor may be evaluated.
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  tensor or OutOfRange.


- - -

### tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None) <div class="md-anchor" id="range_input_producer">{#range_input_producer}</div>

Produces the integers from 0 to limit-1 in a queue.

##### Args:


*  <b>limit</b>: An int32 scalar tensor.
*  <b>num_epochs</b>: An integer (optional). If specified, `range_input_producer`
    produces each integer `num_epochs` times before generating an
    OutOfRange error. If not specified, `range_input_producer` can cycle
    through the integers an unlimited number of times.
*  <b>shuffle</b>: Boolean. If true, the integers are randomly shuffled within each
    epoch.
*  <b>seed</b>: An integer (optional). Seed used if shuffle == True.
*  <b>capacity</b>: An integer. Sets the queue capacity.
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A Queue with the output integers.  A QueueRunner for the Queue
  is added to the current Graph's QUEUE_RUNNER collection.


- - -

### tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None) <div class="md-anchor" id="slice_input_producer">{#slice_input_producer}</div>

Produces a slice of each Tensor in tensor_list.

Implemented using a Queue -- a QueueRunner for the Queue
is added to the current Graph's QUEUE_RUNNER collection.

##### Args:


*  <b>tensor_list</b>: A list of Tensors. Every Tensor in tensor_list must
    have the same size in the first dimension.
*  <b>num_epochs</b>: An integer (optional). If specified, `slice_input_producer`
    produces each slice `num_epochs` times before generating
    an OutOfRange error. If not specified, `slice_input_producer` can cycle
    through the slices an unlimited number of times.
*  <b>seed</b>: An integer (optional). Seed used if shuffle == True.
*  <b>capacity</b>: An integer. Sets the queue capacity.
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A list of tensors, one for each element of tensor_list.  If the tensor
  in tensor_list has shape [N, a, b, .., z], then the corresponding output
  tensor will have shape [a, b, ..., z].


- - -

### tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None) <div class="md-anchor" id="string_input_producer">{#string_input_producer}</div>

Output strings (e.g. filenames) to a queue for an input pipeline.

##### Args:


*  <b>string_tensor</b>: A 1-D string tensor with the strings to produce.
*  <b>num_epochs</b>: An integer (optional). If specified, `string_input_producer`
    produces each string from `string_tensor` `num_epochs` times before
    generating an OutOfRange error. If not specified, `string_input_producer`
    can cycle through the strings in `string_tensor` an unlimited number of
    times.
*  <b>shuffle</b>: Boolean. If true, the strings are randomly shuffled within each
    epoch.
*  <b>seed</b>: An integer (optional). Seed used if shuffle == True.
*  <b>capacity</b>: An integer. Sets the queue capacity.
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A queue with the output strings.  A QueueRunner for the Queue
  is added to the current Graph's QUEUE_RUNNER collection.



### Batching at the end of an input pipeline <div class="md-anchor" id="AUTOGENERATED-batching-at-the-end-of-an-input-pipeline">{#AUTOGENERATED-batching-at-the-end-of-an-input-pipeline}</div>

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 [batch](#batch) or [batch_join](#batch_join) for batching examples that have
already been well shuffled.  Use [shuffle_batch](#shuffle_batch) or
[shuffle_batch_join](#shuffle_batch_join) for examples that
would benefit from additional shuffling.

Use [batch](#batch) or [shuffle_batch](#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
[batch_join](#batch_join) or [shuffle_batch_join](#shuffle_batch_join)
if you have N different subgraphs producing examples to batch and you
want them run by N threads.

- - -

### tf.train.batch(tensor_list, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, name=None) <div class="md-anchor" id="batch">{#batch}</div>

Run tensor_list to fill a queue to create batches.

Implemented using a queue -- a QueueRunner for the queue
is added to the current Graph's QUEUE_RUNNER collection.

##### Args:


*  <b>tensor_list</b>: The list of tensors to enqueue.
*  <b>batch_size</b>: The new batch size pulled from the queue.
*  <b>num_threads</b>: The number of threads enqueuing tensor_list.
*  <b>capacity</b>: Maximum number of elements in the queue, controls the
    how far ahead the prefetching allowed is allowed to get and
    memory usage.
*  <b>enqueue_many</b>: If False, tensor_list is assumed to represent a
    single example.  If True, tensor_list is assumed to represent
    a batch of examples, where the first dimension is indexed by
    example, and all members of tensor_list should have the same
    size in the first dimension.
*  <b>shapes</b>: Optional. The shapes for each example.  Defaults to the
    inferred shapes for tensor_list (leaving off the first dimension
    if enqueue_many is True).
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A list of tensors with the same number and types as tensor_list.
  If enqueue_many is false, then an input tensor with shape
  `[x, y, z]` will be output as a tensor with shape
  `[batch_size, x, y, z]`.  If enqueue_many is True, and an
  input tensor has shape `[*, x, y, z]`, the the output will have
  shape `[batch_size, x, y, z]`.


- - -

### tf.train.batch_join(tensor_list_list, batch_size, capacity=32, enqueue_many=False, shapes=None, name=None) <div class="md-anchor" id="batch_join">{#batch_join}</div>

Run a list of tensors to fill a queue to create batches of examples.

This version enqueues a different list of tensors in different threads.
Implemented using a queue -- a QueueRunner for the queue
is added to the current Graph's QUEUE_RUNNER collection.

##### Args:


*  <b>tensor_list_list</b>: A list of tuples of tensors to enqueue.
    len(tensor_list_list) threads will be started, with the i-th
    thread enqueuing the tensors from tensor_list[i].
    tensor_list[i1][j] must match tensor_list[i2][j] in type and
    shape (except in the first dimension if enqueue_many is true).
*  <b>batch_size</b>: The new batch size pulled from the queue.
*  <b>capacity</b>: Maximum number of elements in the queue, controls the
    how far ahead the prefetching allowed is allowed to get and
    memory usage.
*  <b>enqueue_many</b>: If False, each tensor_list_list[i] is assumed to
    represent a single example.  If True, tensor_list_list[i] is
    assumed to represent a batch of examples, where the first
    dimension is indexed by example, and all members of
    tensor_list_list[i] should have the same size in the first
    dimension.
*  <b>shapes</b>: Optional. The shapes for each example.  Defaults to the
    inferred shapes for tensor_list_list[i] (which must match, after
    leaving off the first dimension if enqueue_many is True).
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A list of tensors with the same number and types as
  tensor_list_list[i].  If enqueue_many is false, then an input
  tensor with shape `[x, y, z]` will be output as a tensor with
  shape `[batch_size, x, y, z]`.  If enqueue_many is True, and an
  input tensor has shape `[*, x, y, z]`, the the output will have
  shape `[batch_size, x, y, z]`.


- - -

### tf.train.shuffle_batch(tensor_list, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, name=None) <div class="md-anchor" id="shuffle_batch">{#shuffle_batch}</div>

Create batches by randomly shuffling tensors.

This adds:

* a shuffling queue into which tensors from tensor_list are enqueued.
* a dequeue many operation to create batches from the queue,
* and a QueueRunner is added to the current Graph's QUEUE_RUNNER collection,
  to enqueue the tensors from tensor_list.

##### Args:


*  <b>tensor_list</b>: The list of tensors to enqueue.
*  <b>batch_size</b>: The new batch size pulled from the queue.
*  <b>capacity</b>: Maximum number of elements in the queue, controls the
    how far ahead the prefetching allowed is allowed to get and
    memory usage.
*  <b>min_after_dequeue</b>: Minimum number elements in the queue after a
    dequeue, used to ensure a level of mixing of elements.
*  <b>num_threads</b>: The number of threads enqueuing tensor_list.
*  <b>seed</b>: Seed for the random shuffling within the queue.
*  <b>enqueue_many</b>: If False, tensor_list is assumed to represent a
    single example.  If True, tensor_list is assumed to represent
    a batch of examples, where the first dimension is indexed by
    example, and all members of tensor_list should have the same
    size in the first dimension.
*  <b>shapes</b>: Optional. The shapes for each example.  Defaults to the
    inferred shapes for tensor_list (leaving off the first dimension
    if enqueue_many is True).
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A list of tensors with the same number and types as tensor_list.
  If enqueue_many is false, then an input tensor with shape
  `[x, y, z]` will be output as a tensor with shape
  `[batch_size, x, y, z]`.  If enqueue_many is True, and an
  input tensor has shape `[*, x, y, z]`, the the output will have
  shape `[batch_size, x, y, z]`.


- - -

### tf.train.shuffle_batch_join(tensor_list_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, name=None) <div class="md-anchor" id="shuffle_batch_join">{#shuffle_batch_join}</div>

Create batches by randomly shuffling tensors.

This version enqueues a different list of tensors in different threads.
It adds:

* a shuffling queue into which tensors from tensor_list_list are enqueued.
* a dequeue many operation to create batches from the queue,
* and a QueueRunner is added to the current Graph's QUEUE_RUNNER collection,
  to enqueue the tensors from tensor_list_list.

##### Args:


*  <b>tensor_list_list</b>: A list of tuples of tensors to enqueue.
    len(tensor_list_list) threads will be started, with the i-th
    thread enqueuing the tensors from tensor_list[i].
    tensor_list[i1][j] must match tensor_list[i2][j] in type and
    shape (except in the first dimension if enqueue_many is true).
*  <b>batch_size</b>: The new batch size pulled from the queue.
*  <b>capacity</b>: Maximum number of elements in the queue, controls the
    how far ahead the prefetching allowed is allowed to get and
    memory usage.
*  <b>min_after_dequeue</b>: Minimum number elements in the queue after a
    dequeue, used to ensure a level of mixing of elements.
*  <b>seed</b>: Seed for the random shuffling within the queue.
*  <b>enqueue_many</b>: If False, each tensor_list_list[i] is assumed to
    represent a single example.  If True, tensor_list_list[i] is
    assumed to represent a batch of examples, where the first
    dimension is indexed by example, and all members of
    tensor_list_list[i] should have the same size in the first
    dimension.
*  <b>shapes</b>: Optional. The shapes for each example.  Defaults to the
    inferred shapes for tensor_list_list[i] (which must match, after
    leaving off the first dimension if enqueue_many is True).
*  <b>name</b>: A name for the operations (optional).

##### Returns:

  A list of tensors with the same number and types as
  tensor_list_list[i].  If enqueue_many is false, then an input
  tensor with shape `[x, y, z]` will be output as a tensor with
  shape `[batch_size, x, y, z]`.  If enqueue_many is True, and an
  input tensor has shape `[*, x, y, z]`, the the output will have
  shape `[batch_size, x, y, z]`.