# Reading data

Note: The preferred way to feed data into a tensorflow program is using the
@{$datasets$Datasets API}.

There are three other methods of getting data into a TensorFlow program:

*   Feeding: Python code provides the data when running each step.
*   Reading from files: an input pipeline reads the data from files
    at the beginning of a TensorFlow graph.
*   Preloaded data: a constant or variable in the TensorFlow graph holds
    all the data (for small data sets).

[TOC]

## Feeding

TensorFlow's feed mechanism lets you inject data into any Tensor in a
computation graph. A python computation can thus feed data directly into the
graph.

Supply feed data through the `feed_dict` argument to a run() or eval() call
that initiates computation.

Note: "Feeding" is the least efficient way to feed data into a tensorflow
program and should only be used for small experiments and debugging.

```python
with tf.Session():
  input = tf.placeholder(tf.float32)
  classifier = ...
  print(classifier.eval(feed_dict={input: my_python_preprocessing_fn()}))
```

While you can replace any Tensor with feed data, including variables and
constants, the best practice is to use a
@{tf.placeholder} node. A
`placeholder` exists solely to serve as the target of feeds. It is not
initialized and contains no data. A placeholder generates an error if
it is executed without a feed, so you won't forget to feed it.

An example using `placeholder` and feeding to train on MNIST data can be found
in
[`tensorflow/examples/tutorials/mnist/fully_connected_feed.py`](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/fully_connected_feed.py),
and is described in the @{$mechanics$MNIST tutorial}.

## Reading from files

A typical pipeline for reading records from files has the following stages:

1.  The list of filenames
2.  *Optional* filename shuffling
3.  *Optional* epoch limit
4.  Filename queue
5.  A Reader for the file format
6.  A decoder for a record read by the reader
7.  *Optional* preprocessing
8.  Example queue

Note: This section discusses implementing input pipelines useing the
queue-based APIs which can be cleanly replaced by the ${$datasets$Dataset API}.

### Filenames, shuffling, and epoch limits

For the list of filenames, use either a constant string Tensor (like
`["file0", "file1"]` or `[("file%d" % i) for i in range(2)]`) or the
@{tf.train.match_filenames_once} function.

Pass the list of filenames to the @{tf.train.string_input_producer} function.
`string_input_producer` creates a FIFO queue for holding the filenames until
the reader needs them.

`string_input_producer` has options for shuffling and setting a maximum number
of epochs. A queue runner adds the whole list of filenames to the queue once
for each epoch, shuffling the filenames within an epoch if `shuffle=True`.
This procedure provides a uniform sampling of files, so that examples are not
under- or over- sampled relative to each other.

The queue runner works in a thread separate from the reader that pulls
filenames from the queue, so the shuffling and enqueuing process does not
block the reader.

### File formats

Select the reader that matches your input file format and pass the filename
queue to the reader's read method.  The read method outputs a key identifying
the file and record (useful for debugging if you have some weird records), and
a scalar string value. Use one (or more) of the decoder and conversion ops to
decode this string into the tensors that make up an example.

#### CSV files

To read text files in [comma-separated value (CSV)
format](https://tools.ietf.org/html/rfc4180), use a
@{tf.TextLineReader} with the
@{tf.decode_csv} operation. For example:

```python
filename_queue = tf.train.string_input_producer(["file0.csv", "file1.csv"])

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[1], [1], [1], [1], [1]]
col1, col2, col3, col4, col5 = tf.decode_csv(
    value, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3, col4])

with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  for i in range(1200):
    # Retrieve a single instance:
    example, label = sess.run([features, col5])

  coord.request_stop()
  coord.join(threads)
```

Each execution of `read` reads a single line from the file. The
`decode_csv` op then parses the result into a list of tensors. The
`record_defaults` argument determines the type of the resulting tensors and
sets the default value to use if a value is missing in the input string.

You must call `tf.train.start_queue_runners` to populate the queue before
you call `run` or `eval` to execute the `read`. Otherwise `read` will
block while it waits for filenames from the queue.

#### Fixed length records

To read binary files in which each record is a fixed number of bytes, use
@{tf.FixedLengthRecordReader}
with the @{tf.decode_raw} operation.
The `decode_raw` op converts from a string to a uint8 tensor.

For example, [the CIFAR-10 dataset](http://www.cs.toronto.edu/~kriz/cifar.html)
uses a file format where each record is represented using a fixed number of
bytes: 1 byte for the label followed by 3072 bytes of image data. Once you have
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}.

#### 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}
containing
[`tf.train.Example` protocol buffers](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
(which contain
[`Features`](https://www.tensorflow.org/code/tensorflow/core/example/feature.proto)
as a field).  You write a little program that gets your data, stuffs it in an
`Example` protocol buffer, serializes the protocol buffer to a string, and then
writes the string to a TFRecords file using the
@{tf.python_io.TFRecordWriter}.
For example,
[`tensorflow/examples/how_tos/reading_data/convert_to_records.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/convert_to_records.py)
converts MNIST data to this format.

To read a file of TFRecords, use
@{tf.TFRecordReader} with
the @{tf.parse_single_example}
decoder. The `parse_single_example` op decodes the example protocol buffers into
tensors. An MNIST example using the data produced by `convert_to_records` can be
found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_reader.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py),
which you can compare with the `fully_connected_feed` version.

### Preprocessing

You can then do any preprocessing of these examples you want. This would be any
processing that doesn't depend on trainable parameters. Examples include
normalization of your data, picking a random slice, adding noise or distortions,
etc.  See
[`tensorflow_models/tutorials/image/cifar10/cifar10_input.py`](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_input.py)
for an example.

### Batching

At the end of the pipeline we use another queue to batch together examples for
training, evaluation, or inference.  For this we use a queue that randomizes the
order of examples, using the
@{tf.train.shuffle_batch}.

Example:

```
def read_my_file_format(filename_queue):
  reader = tf.SomeReader()
  key, record_string = reader.read(filename_queue)
  example, label = tf.some_decoder(record_string)
  processed_example = some_processing(example)
  return processed_example, label

def input_pipeline(filenames, batch_size, num_epochs=None):
  filename_queue = tf.train.string_input_producer(
      filenames, num_epochs=num_epochs, shuffle=True)
  example, label = read_my_file_format(filename_queue)
  # min_after_dequeue defines how big a buffer we will randomly sample
  #   from -- bigger means better shuffling but slower start up and more
  #   memory used.
  # capacity must be larger than min_after_dequeue and the amount larger
  #   determines the maximum we will prefetch.  Recommendation:
  #   min_after_dequeue + (num_threads + a small safety margin) * batch_size
  min_after_dequeue = 10000
  capacity = min_after_dequeue + 3 * batch_size
  example_batch, label_batch = tf.train.shuffle_batch(
      [example, label], batch_size=batch_size, capacity=capacity,
      min_after_dequeue=min_after_dequeue)
  return example_batch, label_batch
```

If you need more parallelism or shuffling of examples between files, use
multiple reader instances using the
@{tf.train.shuffle_batch_join}.
For example:

```
def read_my_file_format(filename_queue):
  # Same as above

def input_pipeline(filenames, batch_size, read_threads, num_epochs=None):
  filename_queue = tf.train.string_input_producer(
      filenames, num_epochs=num_epochs, shuffle=True)
  example_list = [read_my_file_format(filename_queue)
                  for _ in range(read_threads)]
  min_after_dequeue = 10000
  capacity = min_after_dequeue + 3 * batch_size
  example_batch, label_batch = tf.train.shuffle_batch_join(
      example_list, batch_size=batch_size, capacity=capacity,
      min_after_dequeue=min_after_dequeue)
  return example_batch, label_batch
```

You still only use a single filename queue that is shared by all the readers.
That way we ensure that the different readers use different files from the same
epoch until all the files from the epoch have been started.  (It is also usually
sufficient to have a single thread filling the filename queue.)

An alternative is to use a single reader via the
@{tf.train.shuffle_batch}
with `num_threads` bigger than 1.  This will make it read from a single file at
the same time (but faster than with 1 thread), instead of N files at once.
This can be important:

*   If you have more reading threads than input files, to avoid the risk that
    you will have two threads reading the same example from the same file near
    each other.
*   Or if reading N files in parallel causes too many disk seeks.

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}.

### Creating threads to prefetch using `QueueRunner` objects

The short version: many of the `tf.train` functions listed above add
@{tf.train.QueueRunner} objects to your
graph.  These require that you call
@{tf.train.start_queue_runners}
before running any training or inference steps, or it will hang forever. This
will start threads that run the input pipeline, filling the example queue so
that the dequeue to get the examples will succeed.  This is best combined with a
@{tf.train.Coordinator} to cleanly
shut down these threads when there are errors. If you set a limit on the number
of epochs, that will use an epoch counter that will need to be initialized. The
recommended code pattern combining these is:

```python
# Create the graph, etc.
init_op = tf.global_variables_initializer()

# Create a session for running operations in the Graph.
sess = tf.Session()

# Initialize the variables (like the epoch counter).
sess.run(init_op)

# Start input enqueue threads.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)

try:
    while not coord.should_stop():
        # Run training steps or whatever
        sess.run(train_op)

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')
finally:
    # When done, ask the threads to stop.
    coord.request_stop()

# Wait for threads to finish.
coord.join(threads)
sess.close()
```

#### Aside: What is happening here?

First we create the graph. It will have a few pipeline stages that are
connected by queues. The first stage will generate filenames to read and enqueue
them in the filename queue. The second stage consumes filenames (using a
`Reader`), produces examples, and enqueues them in an example queue. Depending
on how you have set things up, you may actually have a few independent copies of
the second stage, so that you can read from multiple files in parallel. At the
end of these stages is an enqueue operation, which enqueues into a queue that
the next stage dequeues from. We want to start threads running these enqueuing
operations, so that our training loop can dequeue examples from the example
queue.

<div style="width:70%; margin-left:12%; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/AnimatedFileQueues.gif">
</div>

The helpers in `tf.train` that create these queues and enqueuing operations add
a @{tf.train.QueueRunner} to the
graph using the
@{tf.train.add_queue_runner}
function. Each `QueueRunner` is responsible for one stage, and holds the list of
enqueue operations that need to be run in threads. Once the graph is
constructed, the
@{tf.train.start_queue_runners}
function asks each QueueRunner in the graph to start its threads running the
enqueuing operations.

If all goes well, you can now run your training steps and the queues will be
filled by the background threads. If you have set an epoch limit, at some point
an attempt to dequeue examples will get an
@{tf.errors.OutOfRangeError}. This
is the TensorFlow equivalent of "end of file" (EOF) -- this means the epoch
limit has been reached and no more examples are available.

The last ingredient is the
@{tf.train.Coordinator}. This is responsible
for letting all the threads know if anything has signaled a shut down. Most
commonly this would be because an exception was raised, for example one of the
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}.

#### Aside: How clean shut-down when limiting epochs works

Imagine you have a model that has set a limit on the number of epochs to train
on.  That means that the thread generating filenames will only run that many
times before generating an `OutOfRange` error. The QueueRunner will catch that
error, close the filename queue, and exit the thread. Closing the queue does two
things:

*   Any future attempt to enqueue in the filename queue will generate an error.
    At this point there shouldn't be any threads trying to do that, but this
    is helpful when queues are closed due to other errors.
*   Any current or future dequeue will either succeed (if there are enough
    elements left) or fail (with an `OutOfRange` error) immediately.  They won't
    block waiting for more elements to be enqueued, since by the previous point
    that can't happen.

The point is that when the filename queue is closed, there will likely still be
many filenames in that queue, so the next stage of the pipeline (with the reader
and other preprocessing) may continue running for some time.  Once the filename
queue is exhausted, though, the next attempt to dequeue a filename (e.g. from a
reader that has finished with the file it was working on) will trigger an
`OutOfRange` error.  In this case, though, you might have multiple threads
associated with a single QueueRunner.  If this isn't the last thread in the
QueueRunner, the `OutOfRange` error just causes the one thread to exit.  This
allows the other threads, which are still finishing up their last file, to
proceed until they finish as well.  (Assuming you are using a
@{tf.train.Coordinator},
other types of errors will cause all the threads to stop.)  Once all the reader
threads hit the `OutOfRange` error, only then does the next queue, the example
queue, gets closed.

Again, the example queue will have some elements queued, so training will
continue until those are exhausted.  If the example queue is a
@{tf.RandomShuffleQueue}, say
because you are using `shuffle_batch` or `shuffle_batch_join`, it normally will
avoid ever having fewer than its `min_after_dequeue` attr elements buffered.
However, once the queue is closed that restriction will be lifted and the queue
will eventually empty.  At that point the actual training threads, when they
try and dequeue from example queue, will start getting `OutOfRange` errors and
exiting.  Once all the training threads are done,
@{tf.train.Coordinator.join}
will return and you can exit cleanly.

### Filtering records or producing multiple examples per record

Instead of examples with shapes `[x, y, z]`, you will produce a batch of
examples with shape `[batch, x, y, z]`.  The batch size can be 0 if you want to
filter this record out (maybe it is in a hold-out set?), or bigger than 1 if you
are producing multiple examples per record.  Then simply set `enqueue_many=True`
when calling one of the batching functions (such as `shuffle_batch` or
`shuffle_batch_join`).

### Sparse input data

SparseTensors don't play well with queues. If you use SparseTensors you have
to decode the string records using
@{tf.parse_example} **after**
batching (instead of using `tf.parse_single_example` before batching).

## Preloaded data

This is only used for small data sets that can be loaded entirely in memory.
There are two approaches:

* Store the data in a constant.
* Store the data in a variable, that you initialize (or assign to) and then
  never change.

Using a constant is a bit simpler, but uses more memory (since the constant is
stored inline in the graph data structure, which may be duplicated a few times).

```python
training_data = ...
training_labels = ...
with tf.Session():
  input_data = tf.constant(training_data)
  input_labels = tf.constant(training_labels)
  ...
```

To instead use a variable, you need to also initialize it after the graph has been built.

```python
training_data = ...
training_labels = ...
with tf.Session() as sess:
  data_initializer = tf.placeholder(dtype=training_data.dtype,
                                    shape=training_data.shape)
  label_initializer = tf.placeholder(dtype=training_labels.dtype,
                                     shape=training_labels.shape)
  input_data = tf.Variable(data_initializer, trainable=False, collections=[])
  input_labels = tf.Variable(label_initializer, trainable=False, collections=[])
  ...
  sess.run(input_data.initializer,
           feed_dict={data_initializer: training_data})
  sess.run(input_labels.initializer,
           feed_dict={label_initializer: training_labels})
```

Setting `trainable=False` keeps the variable out of the
`GraphKeys.TRAINABLE_VARIABLES` collection in the graph, so we won't try and
update it when training.  Setting `collections=[]` keeps the variable out of the
`GraphKeys.GLOBAL_VARIABLES` collection used for saving and restoring checkpoints.

Either way,
@{tf.train.slice_input_producer}
can be used to produce a slice at a time.  This shuffles the examples across an
entire epoch, so further shuffling when batching is undesirable.  So instead of
using the `shuffle_batch` functions, we use the plain
@{tf.train.batch} function.  To use
multiple preprocessing threads, set the `num_threads` parameter to a number
bigger than 1.

An MNIST example that preloads the data using constants can be found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py), and one that preloads the data using variables can be found in
[`tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py`](https://www.tensorflow.org/code/tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py),
You can compare these with the `fully_connected_feed` and
`fully_connected_reader` versions above.

## Multiple input pipelines

Commonly you will want to train on one dataset and evaluate (or "eval") on
another.  One way to do this is to actually have two separate graphs and
sessions, maybe in separate processes:

* The training process reads training input data and periodically writes
  checkpoint files with all the trained variables.
* The evaluation process restores the checkpoint files into an inference
  model that reads validation input data.

This is what is done @{tf.estimator$estimators} and manually in
@{$deep_cnn#save-and-restore-checkpoints$the example CIFAR-10 model}.
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}.

To support the single-graph approach
@{$programmers_guide/datasets$Datasets} also supplies
@{$programmers_guide/datasets#creating_an_iterator$advanced iterator types} that
that allow the user to change the input pipeline without rebuilding the graph or
session.

Note: Regardless of the implementation, many
operations (like ${tf.layers.batch_normalization}, and @{tf.layers.dropout})
need to know if they are in training or evaluation mode, and you must be
careful to set this apropriately if you change the data source.
