There are three main 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]
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.
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
placeholder op 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,
and is described in the MNIST tutorial.
A typical pipeline for reading records from files has the following stages:
- The list of filenames
- Optional filename shuffling
- Optional epoch limit
- Filename queue
- A Reader for the file format
- A decoder for a record read by the reader
- Optional preprocessing
- Example queue
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.
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.
To read text files in comma-separated value (CSV)
format, use a
TextLineReader with the
decode_csv operation. For example:
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.
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
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
and described in
this tutorial.
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
TFRecords file
containing
tf.train.Example protocol buffers
(which contain
Features
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 class.
For example,
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,
which you can compare with the fully_connected_feed version.
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
for an example.
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 function.
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 function.
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 function
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
view your summaries as training progresses using TensorBoard.
The short version: many of the tf.train functions listed above add
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:
# 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()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.
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.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
Coordinator. This is responsible
for letting all the threads know if anything has signalled 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 see here.
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
OutOfRangeerror) 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
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.
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).
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).
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 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).
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.
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 function
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, and one that preloads the data using variables can be found in
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.
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 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 in 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. See the shared variables tutorial.