Variables

Note: 함수의 Tensor 인자는 tf.convert_to_tensor에 의한 것도 가능합니다.

[TOC]

Variables

class tf.Variable

Variables How To에서 자세한 개요를 확인할 수 있습니다.

변수는 graph에서 run()의 호출로 상태를 유지합니다. Variable의 객체를 만들어 graph에 변수를 추가합니다.

Variable() 생성자는 변수의 초기값으로 Tensor의 type과 shape이 필요합니다. 초기값은 변수의 type과 shape를 정의합니다. 생성 후, 변수의 type과 shape은 고정됩니다. 변수의 값은 assign 메소드를 사용해 변경할 수 있습니다.

후에 변수의 shape를 변경하고 싶다면 assign에서 validate_shape=False로 해야합니다.

Tensor의 경우, Variable()로 만들어진 변수는 graph의 ops의 input으로 사용될 수 있습니다. 추가적으로, Tensor 클래스로 오버로드 되는 모든 연산(operators)은 변수로 넘겨집니다. 그래서 변수의 산술연산만으로도 graph에 노드를 추가할 수 있습니다.

import tensorflow as tf

# Create a variable.
w = tf.Variable(<initial-value>, name=<optional-name>)

# Use the variable in the graph like any Tensor.
y = tf.matmul(w, ...another variable or tensor...)

# The overloaded operators are available too.
z = tf.sigmoid(w + y)

# Assign a new value to the variable with `assign()` or a related method.
w.assign(w + 1.0)
w.assign_add(1.0)

graph를 실행할 때, 변수는 그 값을 사용하는 ops를 실행하기 전에 명시적으로 초기화해야 합니다. 변수는 1)initializer op를 실행하거나, 2)저장된 파일로부터 변수를 다시 저장(restoring)하거나, 3)간단하게 변수에 값을 할당하는 assign Op을 실행하여 초기화 할 수 있습니다. 사실, 변수 initializer op는 단지 변수의 초기값을 할당하는 assign Op 입니다.

# Launch the graph in a session.
with tf.Session() as sess:
    # Run the variable initializer.
    sess.run(w.initializer)
    # ...you now can run ops that use the value of 'w'...

가장 일반적인 초기화 방법은 모든 변수를 초기화 할 graph에 편의 함수인 initialize_all_variables()으로 Op를 추가하는 것 입니다. 그런 다음 graph를 실행한 후 Op를 실행합니다.

# Add an Op to initialize all variables.
init_op = tf.initialize_all_variables()

# Launch the graph in a session.
with tf.Session() as sess:
    # Run the Op that initializes all variables.
    sess.run(init_op)
    # ...you can now run any Op that uses variable values...

다른 변수의 결과로 초기되는 변수를 만들어야한다면, 다른 변수의 initialized_value()를 사용합니다. 이것은 변수가 올바는 순서로 초기화되도록 합니다.

모든 변수들은 자동적으로 그들이 만들어진 graph에 쌓입니다. 기본적으로, 생성자는 그래프 컬렉션(graph collection) GraphKeys.VARIABLES에 변수를 추가합니다. 편의 함수인 all_variables()은 컬렉션의 내용을 반환합니다.

머신 러닝 모델을 만들 때, 학습 가능한 모델 매개변수를 가지고 있는 변수와 global step 변수과 같이 학습 단계를 계산하기 위한 다른 변수로 구분하는 것은 종종 편리합니다. 이것을 더 쉽게 하기위해, 변수 생성자는 trainable=<bool> 매개변수를 지원합니다. True일 때 새로운 변수는 그래프 컬렉션 GraphKeys.TRAINABLE_VARIABLES에 추가됩니다. 편의 함수 trainable_variables()는 이 컬렉션의 내용을 반환합니다. 다양한 Optimizer 클래스는 이 컬렉션을 최적화(optimize) 변수의 기본 리스트로 사용합니다.

Creating a variable.

tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None)

Creates a new variable with value initial_value.

The new variable is added to the graph collections listed in collections, which defaults to [GraphKeys.VARIABLES].

If trainable is True the variable is also added to the graph collection GraphKeys.TRAINABLE_VARIABLES.

This constructor creates both a variable Op and an assign Op to set the variable to its initial value.

Args:
  • initial_value: A Tensor, or Python object convertible to a Tensor, which is the initial value for the Variable. The initial value must have a shape specified unless validate_shape is set to False. Can also be a callable with no argument that returns the initial value when called. In that case, dtype must be specified. (Note that initializer functions from init_ops.py must first be bound to a shape before being used here.)
  • trainable: If True, the default, also adds the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES. This collection is used as the default list of variables to use by the Optimizer classes.
  • collections: List of graph collections keys. The new variable is added to these collections. Defaults to [GraphKeys.VARIABLES].
  • validate_shape: If False, allows the variable to be initialized with a value of unknown shape. If True, the default, the shape of initial_value must be known.
  • caching_device: Optional device string describing where the Variable should be cached for reading. Defaults to the Variable's device. If not None, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through Switch and other conditional statements.
  • name: Optional name for the variable. Defaults to 'Variable' and gets uniquified automatically.
  • variable_def: VariableDef protocol buffer. If not None, recreates the Variable object with its contents. variable_def and the other arguments are mutually exclusive.
  • dtype: If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor), or convert_to_tensor will decide.
Returns:

A Variable.

Raises:
  • ValueError: If both variable_def and initial_value are specified.
  • ValueError: If the initial value is not specified, or does not have a shape and validate_shape is True.

tf.Variable.initialized_value()

Returns the value of the initialized variable.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

# Initialize 'v' with a random tensor.
v = tf.Variable(tf.truncated_normal([10, 40]))
# Use `initialized_value` to guarantee that `v` has been
# initialized before its value is used to initialize `w`.
# The random values are picked only once.
w = tf.Variable(v.initialized_value() * 2.0)
Returns:

A Tensor holding the value of this variable after its initializer has run.

Changing a variable value.

tf.Variable.assign(value, use_locking=False)

Assigns a new value to the variable.

This is essentially a shortcut for assign(self, value).

Args:
  • value: A Tensor. The new value for this variable.
  • use_locking: If True, use locking during the assignment.
Returns:

A Tensor that will hold the new value of this variable after the assignment has completed.

tf.Variable.assign_add(delta, use_locking=False)

Adds a value to this variable.

This is essentially a shortcut for assign_add(self, delta).

Args:
  • delta: A Tensor. The value to add to this variable.
  • use_locking: If True, use locking during the operation.
Returns:

A Tensor that will hold the new value of this variable after the addition has completed.

tf.Variable.assign_sub(delta, use_locking=False)

Subtracts a value from this variable.

This is essentially a shortcut for assign_sub(self, delta).

Args:
  • delta: A Tensor. The value to subtract from this variable.
  • use_locking: If True, use locking during the operation.
Returns:

A Tensor that will hold the new value of this variable after the subtraction has completed.

tf.Variable.scatter_sub(sparse_delta, use_locking=False)

Subtracts IndexedSlices from this variable.

This is essentially a shortcut for scatter_sub(self, sparse_delta.indices, sparse_delta.values).

Args:
  • sparse_delta: IndexedSlices to be subtracted from this variable.
  • use_locking: If True, use locking during the operation.
Returns:

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises:
  • ValueError: if sparse_delta is not an IndexedSlices.

tf.Variable.count_up_to(limit)

Increments this variable until it reaches limit.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Args:
  • limit: value at which incrementing the variable raises an error.
Returns:

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

tf.Variable.eval(session=None)

In a session, computes and returns the value of this variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See the Session class for more information on launching a graph and on sessions.

v = tf.Variable([1, 2])
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    # Usage passing the session explicitly.
    print(v.eval(sess))
    # Usage with the default session.  The 'with' block
    # above makes 'sess' the default session.
    print(v.eval())
Args:
  • session: The session to use to evaluate this variable. If none, the default session is used.
Returns:

A numpy ndarray with a copy of the value of this variable.

Properties.

tf.Variable.name

The name of this variable.

tf.Variable.dtype

The DType of this variable.

tf.Variable.get_shape()

The TensorShape of this variable.

Returns:

A TensorShape.

tf.Variable.device

The device of this variable.

tf.Variable.initializer

The initializer operation for this variable.

tf.Variable.graph

The Graph of this variable.

tf.Variable.op

The Operation of this variable.

Other Methods

tf.Variable.from_proto(variable_def)

Returns a Variable object created from variable_def.

tf.Variable.initial_value

Returns the Tensor used as the initial value for the variable.

Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.

Returns:

A Tensor.

tf.Variable.ref()

Returns a reference to this variable.

You usually do not need to call this method as all ops that need a reference to the variable call it automatically.

Returns is a Tensor which holds a reference to the variable. You can assign a new value to the variable by passing the tensor to an assign op. See value() if you want to get the value of the variable.

Returns:

A Tensor that is a reference to the variable.

tf.Variable.to_proto()

Converts a Variable to a VariableDef protocol buffer.

Returns:

A VariableDef protocol buffer.

tf.Variable.value()

Returns the last snapshot of this variable.

You usually do not need to call this method as all ops that need the value of the variable call it automatically through a convert_to_tensor() call.

Returns a Tensor which holds the value of the variable. You can not assign a new value to this tensor as it is not a reference to the variable. See ref() if you want to get a reference to the variable.

To avoid copies, if the consumer of the returned value is on the same device as the variable, this actually returns the live value of the variable, not a copy. Updates to the variable are seen by the consumer. If the consumer is on a different device it will get a copy of the variable.

Returns:

A Tensor containing the value of the variable.

Variable helper functions

TensorFlow provides a set of functions to help manage the set of variables collected in the graph.

tf.all_variables()

Returns all variables that must be saved/restored.

The Variable() constructor automatically adds new variables to the graph collection GraphKeys.VARIABLES. This convenience function returns the contents of that collection.

Returns:

A list of Variable objects.

tf.trainable_variables()

Returns all variables created with trainable=True.

When passed trainable=True, the Variable() constructor automatically adds new variables to the graph collection GraphKeys.TRAINABLE_VARIABLES. This convenience function returns the contents of that collection.

Returns:

A list of Variable objects.

tf.local_variables()

Returns all variables created with collection=[LOCAL_VARIABLES].

Returns:

A list of local Variable objects.

tf.moving_average_variables()

Returns all variables that maintain their moving averages.

If an ExponentialMovingAverage object is created and the apply() method is called on a list of variables, these variables will be added to the GraphKeys.MOVING_AVERAGE_VARIABLES collection. This convenience function returns the contents of that collection.

Returns:

A list of Variable objects.

tf.initialize_all_variables()

Returns an Op that initializes all variables.

This is just a shortcut for initialize_variables(all_variables())

Returns:

An Op that initializes all variables in the graph.

tf.initialize_variables(var_list, name='init')

Returns an Op that initializes a list of variables.

After you launch the graph in a session, you can run the returned Op to initialize all the variables in var_list. This Op runs all the initializers of the variables in var_list in parallel.

Calling initialize_variables() is equivalent to passing the list of initializers to Group().

If var_list is empty, however, the function still returns an Op that can be run. That Op just has no effect.

Args:
  • var_list: List of Variable objects to initialize.
  • name: Optional name for the returned operation.
Returns:

An Op that run the initializers of all the specified variables.

tf.initialize_local_variables()

Returns an Op that initializes all local variables.

This is just a shortcut for initialize_variables(local_variables())

Returns:

An Op that initializes all local variables in the graph.

tf.is_variable_initialized(variable)

Tests if a variable has been initialized.

Args:
  • variable: A Variable.
Returns:

Returns a scalar boolean Tensor, True if the variable has been initialized, False otherwise.

tf.report_uninitialized_variables(var_list=None, name='report_uninitialized_variables')

Adds ops to list the names of uninitialized variables.

When run, it returns a 1-D tensor containing the names of uninitialized variables if there are any, or an empty array if there are none.

Args:
  • var_list: List of Variable objects to check. Defaults to the value of all_variables() + local_variables()
  • name: Optional name of the Operation.
Returns:

A 1-D tensor containing names of the unintialized variables, or an empty 1-D tensor if there are no variables or no uninitialized variables.

tf.assert_variables_initialized(var_list=None)

Returns an Op to check if variables are initialized.

NOTE: This function is obsolete and will be removed in 6 months. Please change your implementation to use report_uninitialized_variables().

When run, the returned Op will raise the exception FailedPreconditionError if any of the variables has not yet been initialized.

Note: This function is implemented by trying to fetch the values of the variables. If one of the variables is not initialized a message may be logged by the C++ runtime. This is expected.

Args:
  • var_list: List of Variable objects to check. Defaults to the value of all_variables().
Returns:

An Op, or None if there are no variables.

Saving and Restoring Variables

class tf.train.Saver

Saves and restores variables.

See Variables for an overview of variables, saving and restoring.

The Saver class adds ops to save and restore variables to and from checkpoints. It also provides convenience methods to run these ops.

Checkpoints are binary files in a proprietary format which map variable names to tensor values. The best way to examine the contents of a checkpoint is to load it using a Saver.

Savers can automatically number checkpoint filenames with a provided counter. This lets you keep multiple checkpoints at different steps while training a model. For example you can number the checkpoint filenames with the training step number. To avoid filling up disks, savers manage checkpoint files automatically. For example, they can keep only the N most recent files, or one checkpoint for every N hours of training.

You number checkpoint filenames by passing a value to the optional global_step argument to save():

saver.save(sess, 'my-model', global_step=0) ==> filename: 'my-model-0'
...
saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000'

Additionally, optional arguments to the Saver() constructor let you control the proliferation of checkpoint files on disk:

  • max_to_keep indicates the maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)

  • keep_checkpoint_every_n_hours: In addition to keeping the most recent max_to_keep checkpoint files, you might want to keep one checkpoint file for every N hours of training. This can be useful if you want to later analyze how a model progressed during a long training session. For example, passing keep_checkpoint_every_n_hours=2 ensures that you keep one checkpoint file for every 2 hours of training. The default value of 10,000 hours effectively disables the feature.

Note that you still have to call the save() method to save the model. Passing these arguments to the constructor will not save variables automatically for you.

A training program that saves regularly looks like:

...
# Create a saver.
saver = tf.train.Saver(...variables...)
# Launch the graph and train, saving the model every 1,000 steps.
sess = tf.Session()
for step in xrange(1000000):
    sess.run(..training_op..)
    if step % 1000 == 0:
        # Append the step number to the checkpoint name:
        saver.save(sess, 'my-model', global_step=step)

In addition to checkpoint files, savers keep a protocol buffer on disk with the list of recent checkpoints. This is used to manage numbered checkpoint files and by latest_checkpoint(), which makes it easy to discover the path to the most recent checkpoint. That protocol buffer is stored in a file named 'checkpoint' next to the checkpoint files.

If you create several savers, you can specify a different filename for the protocol buffer file in the call to save().

tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None)

Creates a Saver.

The constructor adds ops to save and restore variables.

var_list specifies the variables that will be saved and restored. It can be passed as a dict or a list:

  • A dict of names to variables: The keys are the names that will be used to save or restore the variables in the checkpoint files.
  • A list of variables: The variables will be keyed with their op name in the checkpoint files.

For example:

v1 = tf.Variable(..., name='v1')
v2 = tf.Variable(..., name='v2')

# Pass the variables as a dict:
saver = tf.train.Saver({'v1': v1, 'v2': v2})

# Or pass them as a list.
saver = tf.train.Saver([v1, v2])
# Passing a list is equivalent to passing a dict with the variable op names
# as keys:
saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})

The optional reshape argument, if True, allows restoring a variable from a save file where the variable had a different shape, but the same number of elements and type. This is useful if you have reshaped a variable and want to reload it from an older checkpoint.

The optional sharded argument, if True, instructs the saver to shard checkpoints per device.

Args:
  • var_list: A list of Variable objects or a dictionary mapping names to variables. If None, defaults to the list of all variables.
  • reshape: If True, allows restoring parameters from a checkpoint where the variables have a different shape.
  • sharded: If True, shard the checkpoints, one per device.
  • max_to_keep: Maximum number of recent checkpoints to keep. Defaults to 5.
  • keep_checkpoint_every_n_hours: How often to keep checkpoints. Defaults to 10,000 hours.
  • name: String. Optional name to use as a prefix when adding operations.
  • restore_sequentially: A Bool, which if true, causes restore of different variables to happen sequentially within each device. This can lower memory usage when restoring very large models.
  • saver_def: Optional SaverDef proto to use instead of running the builder. This is only useful for specialty code that wants to recreate a Saver object for a previously built Graph that had a Saver. The saver_def proto should be the one returned by the as_saver_def() call of the Saver that was created for that Graph.
  • builder: Optional SaverBuilder to use if a saver_def was not provided. Defaults to BaseSaverBuilder().
Raises:
  • TypeError: If var_list is invalid.
  • ValueError: If any of the keys or values in var_list are not unique.

tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None, meta_graph_suffix='meta', write_meta_graph=True)

Saves variables.

This method runs the ops added by the constructor for saving variables. It requires a session in which the graph was launched. The variables to save must also have been initialized.

The method returns the path of the newly created checkpoint file. This path can be passed directly to a call to restore().

Args:
  • sess: A Session to use to save the variables.
  • save_path: String. Path to the checkpoint filename. If the saver is sharded, this is the prefix of the sharded checkpoint filename.
  • global_step: If provided the global step number is appended to save_path to create the checkpoint filename. The optional argument can be a Tensor, a Tensor name or an integer.
  • latest_filename: Optional name for the protocol buffer file that will contains the list of most recent checkpoint filenames. That file, kept in the same directory as the checkpoint files, is automatically managed by the saver to keep track of recent checkpoints. Defaults to 'checkpoint'.
  • meta_graph_suffix: Suffix for MetaGraphDef file. Defaults to 'meta'.
  • write_meta_graph: Boolean indicating whether or not to write the meta graph file.
Returns:

A string: path at which the variables were saved. If the saver is sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn' is the number of shards created.

Raises:
  • TypeError: If sess is not a Session.
  • ValueError: If latest_filename contains path components.

tf.train.Saver.restore(sess, save_path)

Restores previously saved variables.

This method runs the ops added by the constructor for restoring variables. It requires a session in which the graph was launched. The variables to restore do not have to have been initialized, as restoring is itself a way to initialize variables.

The save_path argument is typically a value previously returned from a save() call, or a call to latest_checkpoint().

Args:
  • sess: A Session to use to restore the parameters.
  • save_path: Path where parameters were previously saved.
Raises:
  • ValueError: If the given save_path does not point to a file.

Other utility methods.

tf.train.Saver.last_checkpoints

List of not-yet-deleted checkpoint filenames.

You can pass any of the returned values to restore().

Returns:

A list of checkpoint filenames, sorted from oldest to newest.

tf.train.Saver.set_last_checkpoints(last_checkpoints)

DEPRECATED: Use set_last_checkpoints_with_time.

Sets the list of old checkpoint filenames.

Args:
  • last_checkpoints: A list of checkpoint filenames.
Raises:
  • AssertionError: If last_checkpoints is not a list.

tf.train.Saver.as_saver_def()

Generates a SaverDef representation of this saver.

Returns:

A SaverDef proto.

Other Methods

tf.train.Saver.export_meta_graph(filename=None, collection_list=None, as_text=False)

Writes MetaGraphDef to save_path/filename.

Args:
  • filename: Optional meta_graph filename including the path.
  • collection_list: List of string keys to collect.
  • as_text: If True, writes the meta_graph as an ASCII proto.
Returns:

A MetaGraphDef proto.

tf.train.Saver.from_proto(saver_def)

Returns a Saver object created from saver_def.

tf.train.Saver.set_last_checkpoints_with_time(last_checkpoints_with_time)

Sets the list of old checkpoint filenames and timestamps.

Args:
  • last_checkpoints_with_time: A list of tuples of checkpoint filenames and timestamps.
Raises:
  • AssertionError: If last_checkpoints_with_time is not a list.

tf.train.Saver.to_proto()

Converts this Saver to a SaverDef protocol buffer.

Returns:

A SaverDef protocol buffer.

tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)

Finds the filename of latest saved checkpoint file.

Args:
  • checkpoint_dir: Directory where the variables were saved.
  • latest_filename: Optional name for the protocol buffer file that contains the list of most recent checkpoint filenames. See the corresponding argument to Saver.save().
Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)

Returns CheckpointState proto from the "checkpoint" file.

If the "checkpoint" file contains a valid CheckpointState proto, returns it.

Args:
  • checkpoint_dir: The directory of checkpoints.
  • latest_filename: Optional name of the checkpoint file. Default to 'checkpoint'.
Returns:

A CheckpointState if the state was available, None otherwise.

tf.train.update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, latest_filename=None)

Updates the content of the 'checkpoint' file.

This updates the checkpoint file containing a CheckpointState proto.

Args:
  • save_dir: Directory where the model was saved.
  • model_checkpoint_path: The checkpoint file.
  • all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted checkpoints, sorted from oldest to newest. If this is a non-empty list, the last element must be equal to model_checkpoint_path. These paths are also saved in the CheckpointState proto.
  • latest_filename: Optional name of the checkpoint file. Default to 'checkpoint'.
Raises:
  • RuntimeError: If the save paths conflict.

Sharing Variables

TensorFlow provides several classes and operations that you can use to create variables contingent on certain conditions.

tf.get_variable(name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)

Gets an existing variable with these parameters or create a new one.

This function prefixes the name with the current variable scope and performs reuse checks. See the Variable Scope How To for an extensive description of how reusing works. Here is a basic example:

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])  # v.name == "foo/v:0"
    w = tf.get_variable("w", [1])  # w.name == "foo/w:0"
with tf.variable_scope("foo", reuse=True)
    v1 = tf.get_variable("v")  # The same as v above.

If initializer is None (the default), the default initializer passed in the variable scope will be used. If that one is None too, a UniformUnitScalingInitializer will be used. The initializer can also be a Tensor, in which case the variable is initialized to this value and shape.

Similarly, if the regularizer is None (the default), the default regularizer passed in the variable scope will be used (if that is None too, then by default no regularization is performed).

If a partitioner is provided, first a sharded Variable is created via _get_partitioned_variable, and the return value is a Tensor composed of the shards concatenated along the partition axis.

Some useful partitioners are available. See, e.g., variable_axis_size_partitioner.

Args:
  • name: The name of the new or existing variable.
  • shape: Shape of the new or existing variable.
  • dtype: Type of the new or existing variable (defaults to DT_FLOAT).
  • initializer: Initializer for the variable if one is created.
  • regularizer: A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
  • trainable: If True also add the variable to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • collections: List of graph collections keys to add the Variable to. Defaults to [GraphKeys.VARIABLES] (see tf.Variable).
  • caching_device: Optional device string or function describing where the Variable should be cached for reading. Defaults to the Variable's device. If not None, caches on another device. Typical use is to cache on the device where the Ops using the Variable reside, to deduplicate copying through Switch and other conditional statements.
  • partitioner: Optional callable that accepts a fully defined TensorShape and dtype of the Variable to be created, and returns a list of partitions for each axis (currently only one axis can be partitioned).
  • validate_shape: If False, allows the variable to be initialized with a value of unknown shape. If True, the default, the shape of initial_value must be known.
Returns:

The created or existing variable.

Raises:
  • ValueError: when creating a new variable and shape is not declared, or when violating reuse during variable creation. Reuse is set inside variable_scope.

class tf.VariableScope

Variable scope object to carry defaults to provide to get_variable.

Many of the arguments we need for get_variable in a variable store are most easily handled with a context. This object is used for the defaults.

Attributes: name: name of the current scope, used as prefix in get_variable. initializer: default initializer passed to get_variable. regularizer: default regularizer passed to get_variable. reuse: Boolean or None, setting the reuse in get_variable. caching_device: string, callable, or None: the caching device passed to get_variable. partitioner: callable or None: the partitioner passed to get_variable. name_scope: The name passed to tf.name_scope.

tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, partitioner=None, name_scope='')

Creates a new VariableScope with the given properties.

tf.VariableScope.caching_device

tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)

Gets an existing variable with this name or create a new one.

tf.VariableScope.initializer

tf.VariableScope.name

tf.VariableScope.partitioner

tf.VariableScope.regularizer

tf.VariableScope.reuse

tf.VariableScope.reuse_variables()

Reuse variables in this scope.

tf.VariableScope.set_caching_device(caching_device)

Set caching_device for this scope.

tf.VariableScope.set_initializer(initializer)

Set initializer for this scope.

tf.VariableScope.set_partitioner(partitioner)

Set partitioner for this scope.

tf.VariableScope.set_regularizer(regularizer)

Set regularizer for this scope.

tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None, caching_device=None, partitioner=None)

Returns a context for variable scope.

Variable scope allows to create new variables and to share already created ones while providing checks to not create or share by accident. For details, see the Variable Scope How To, here we present only a few basic examples.

Simple example of how to create a new variable:

with tf.variable_scope("foo"):
    with tf.variable_scope("bar"):
        v = tf.get_variable("v", [1])
        assert v.name == "foo/bar/v:0"

Basic example of sharing a variable:

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
    v1 = tf.get_variable("v", [1])
assert v1 == v

Sharing a variable by capturing a scope and setting reuse:

with tf.variable_scope("foo") as scope:
    v = tf.get_variable("v", [1])
    scope.reuse_variables()
    v1 = tf.get_variable("v", [1])
assert v1 == v

To prevent accidental sharing of variables, we raise an exception when getting an existing variable in a non-reusing scope.

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])
    v1 = tf.get_variable("v", [1])
    #  Raises ValueError("... v already exists ...").

Similarly, we raise an exception when trying to get a variable that does not exist in reuse mode.

with tf.variable_scope("foo", reuse=True):
    v = tf.get_variable("v", [1])
    #  Raises ValueError("... v does not exists ...").

Note that the reuse flag is inherited: if we open a reusing scope, then all its sub-scopes become reusing as well.

Args:
  • name_or_scope: string or VariableScope: the scope to open.
  • reuse: True or None; if True, we go into reuse mode for this scope as well as all sub-scopes; if None, we just inherit the parent scope reuse.
  • initializer: default initializer for variables within this scope.
  • regularizer: default regularizer for variables within this scope.
  • caching_device: default caching device for variables within this scope.
  • partitioner: default partitioner for variables within this scope.
Returns:

A scope that can be to captured and reused.

Raises:
  • ValueError: when trying to reuse within a create scope, or create within a reuse scope, or if reuse is not None or True.
  • TypeError: when the types of some arguments are not appropriate.

tf.variable_op_scope(values, name_or_scope, default_name=None, initializer=None, regularizer=None, caching_device=None, partitioner=None, reuse=None)

Returns a context manager for defining an op that creates variables.

This context manager validates that the given values are from the same graph, ensures that graph is the default graph, and pushes a name scope and a variable scope.

If name_or_scope is not None, it is used as is in the variable scope. If scope is None, then default_name is used. In that case, if the same name has been previously used in the same scope, it will made unique be appending _N to it.

This is intended to be used when defining generic ops and so reuse is always inherited.

For example, to define a new Python op called my_op_with_vars:

def my_op_with_vars(a, b, scope=None):
  with tf.variable_op_scope([a, b], scope, "MyOp") as scope:
    a = tf.convert_to_tensor(a, name="a")
    b = tf.convert_to_tensor(b, name="b")
    c = tf.get_variable('c')
    # Define some computation that uses `a`, `b`, and `c`.
    return foo_op(..., name=scope)
Args:
  • values: The list of Tensor arguments that are passed to the op function.
  • name_or_scope: The name argument that is passed to the op function, this name_or_scope is not uniquified in the variable scope.
  • default_name: The default name to use if the name_or_scope argument is None, this name will be uniquified. If name_or_scope is provided it won't be used and therefore it is not required and can be None.
  • initializer: The default initializer to pass to variable scope.
  • regularizer: The default regularizer for variables within this scope.
  • caching_device: The default caching device for variables within this scope.
  • partitioner: The default partitioner for variables within this scope.
  • reuse: True or None; if True, we go into reuse mode for this scope as well as all sub-scopes; if None, we just inherit the parent scope reuse.
Returns:

A context manager for use in defining a Python op.

Raises:
  • ValueError: when trying to reuse within a create scope, or create within a reuse scope, or if reuse is not None or True.
  • TypeError: when the types of some arguments are not appropriate.

tf.get_variable_scope()

Returns the current variable scope.

tf.make_template(name_, func_, create_scope_now_=False, **kwargs)

Given an arbitrary function, wrap it so that it does variable sharing.

This wraps func_ in a Template and partially evaluates it. Templates are functions that create variables the first time they are called and reuse them thereafter. In order for func_ to be compatible with a Template it must have the following properties:

  • The function should create all trainable variables and any variables that should be reused by calling tf.get_variable. If a trainable variable is created using tf.Variable, then a ValueError will be thrown. Variables that are intended to be locals can be created by specifying tf.Variable(..., trainable=false).
  • The function may use variable scopes and other templates internally to create and reuse variables, but it shouldn't use tf.get_variables to capture variables that are defined outside of the scope of the function.
  • Internal scopes and variable names should not depend on any arguments that are not supplied to make_template. In general you will get a ValueError telling you that you are trying to reuse a variable that doesn't exist if you make a mistake.

In the following example, both z and w will be scaled by the same y. It is important to note that if we didn't assign scalar_name and used a different name for z and w that a ValueError would be thrown because it couldn't reuse the variable.

def my_op(x, scalar_name):
  var1 = tf.get_variable(scalar_name,
                         shape=[],
                         initializer=tf.constant_initializer(1))
  return x * var1

scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')

z = scale_by_y(input1)
w = scale_by_y(input2)

As a safe-guard, the returned function will raise a ValueError after the first call if trainable variables are created by calling tf.Variable.

If all of these are true, then 2 properties are enforced by the template:

  1. Calling the same template multiple times will share all non-local variables.
  2. Two different templates are guaranteed to be unique, unless you reenter the same variable scope as the initial definition of a template and redefine it. An examples of this exception:
def my_op(x, scalar_name):
  var1 = tf.get_variable(scalar_name,
                         shape=[],
                         initializer=tf.constant_initializer(1))
  return x * var1

with tf.variable_scope('scope') as vs:
  scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
  z = scale_by_y(input1)
  w = scale_by_y(input2)

# Creates a template that reuses the variables above.
with tf.variable_scope(vs, reuse=True):
  scale_by_y2 = tf.make_template('scale_by_y', my_op, scalar_name='y')
  z2 = scale_by_y2(input1)
  w2 = scale_by_y2(input2)

Depending on the value of create_scope_now_, the full variable scope may be captured either at the time of first call or at the time of construction. If this option is set to True, then all Tensors created by repeated calls to the template will have an extra trailing _N+1 to their name, as the first time the scope is entered in the Template constructor no Tensors are created.

Note: name_, func_ and create_scope_now_ have a trailing underscore to reduce the likelihood of collisions with kwargs.

Args:
  • name_: A name for the scope created by this template. If necessary, the name will be made unique by appending _N to the name.
  • func_: The function to wrap.
  • create_scope_now_: Boolean controlling whether the scope should be created when the template is constructed or when the template is called. Default is False, meaning the scope is created when the template is called.
  • **kwargs: Keyword arguments to apply to func_.
Returns:

A function to encapsulate a set of variables which should be created once and reused. An enclosing scope will created, either where make_template is called, or wherever the result is called, depending on the value of create_scope_now_. Regardless of the value, the first time the template is called it will enter the scope with no reuse, and call func_ to create variables, which are guaranteed to be unique. All subsequent calls will re-enter the scope and reuse those variables.

Raises:
  • ValueError: if the name is None.

tf.no_regularizer(_)

Use this function to prevent regularization of variables.

tf.constant_initializer(value=0.0, dtype=tf.float32)

Returns an initializer that generates tensors with a single value.

Args:
  • value: A Python scalar. All elements of the initialized variable will be set to this value.
  • dtype: The data type. Only floating point types are supported.
Returns:

An initializer that generates tensors with a single value.

Raises:
  • ValueError: if dtype is not a floating point type.

tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)

Returns an initializer that generates tensors with a normal distribution.

Args:
  • mean: a python scalar or a scalar tensor. Mean of the random values to generate.
  • stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate.
  • seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.
Returns:

An initializer that generates tensors with a normal distribution.

Raises:
  • ValueError: if dtype is not a floating point type.

tf.truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)

Returns an initializer that generates a truncated normal distribution.

These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

Args:
  • mean: a python scalar or a scalar tensor. Mean of the random values to generate.
  • stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate.
  • seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.
Returns:

An initializer that generates tensors with a truncated normal distribution.

Raises:
  • ValueError: if dtype is not a floating point type.

tf.random_uniform_initializer(minval=0.0, maxval=1.0, seed=None, dtype=tf.float32)

Returns an initializer that generates tensors with a uniform distribution.

Args:
  • minval: a python scalar or a scalar tensor. lower bound of the range of random values to generate.
  • maxval: a python scalar or a scalar tensor. upper bound of the range of random values to generate.
  • seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.
Returns:

An initializer that generates tensors with a uniform distribution.

Raises:
  • ValueError: if dtype is not a floating point type.

tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32, full_shape=None)

Returns an initializer that generates tensors without scaling variance.

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. If the input is x and the operation x * W, and we want to initialize W uniformly at random, we need to pick W from

[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]

to keep the scale intact, where dim = W.shape[0] (the size of the input). A similar calculation for convolutional networks gives an analogous result with dim equal to the product of the first 3 dimensions. When nonlinearities are present, we need to multiply this by a constant factor. See Sussillo et al., 2014 (pdf) for deeper motivation, experiments and the calculation of constants. In section 2.3 there, the constants were numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.

If the shape tuple full_shape is provided, the scale will be calculated from this predefined shape. This is useful when a Variable is being partitioned across several shards, and each shard has a smaller shape than the whole. Since the shards are usually concatenated when used, the scale should be based on the shape of the whole.

Args:
  • factor: Float. A multiplicative factor by which the values will be scaled.
  • seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
  • dtype: The data type. Only floating point types are supported.
  • full_shape: Tuple or list of integers. The shape used for calculating scale normalization (instead of the shape passed at creation time). Useful when creating sharded variables via partitioning.
Returns:

An initializer that generates tensors with unit variance.

Raises:
  • ValueError: if dtype is not a floating point type.

tf.zeros_initializer(shape, dtype=tf.float32)

An adaptor for zeros() to match the Initializer spec.

tf.ones_initializer(shape, dtype=tf.float32)

An adaptor for ones() to match the Initializer spec.

Variable Partitioners for Sharding

tf.variable_axis_size_partitioner(max_shard_bytes, axis=0, bytes_per_string_element=16, max_shards=None)

Get a partitioner for VariableScope to keep shards below max_shard_bytes.

This partitioner will shard a Variable along one axis, attempting to keep the maximum shard size below max_shard_bytes. In practice, this is not always possible when sharding along only one axis. When this happens, this axis is sharded as much as possible (i.e., every dimension becomes a separate shard).

If the partitioner hits the max_shards limit, then each shard may end up larger than max_shard_bytes. By default max_shards equals None and no limit on the number of shards is enforced.

One reasonable value for max_shard_bytes is (64 << 20) - 1, or almost 64MB, to keep below the protobuf byte limit.

Args:
  • max_shard_bytes: The maximum size any given shard is allowed to be.
  • axis: The axis to partition along. Default: outermost axis.
  • bytes_per_string_element: If the Variable is of type string, this provides an estimate of how large each scalar in the Variable is.
  • max_shards: The maximum number of shards in int created taking precedence over max_shard_bytes.
Returns:

A partition function usable as the partitioner argument to variable_scope, get_variable, and get_partitioned_variable_list.

Raises:
  • ValueError: If any of the byte counts are non-positive.

Sparse Variable Updates

The sparse update ops modify a subset of the entries in a dense Variable, either overwriting the entries or adding / subtracting a delta. These are useful for training embedding models and similar lookup-based networks, since only a small subset of embedding vectors change in any given step.

Since a sparse update of a large tensor may be generated automatically during gradient computation (as in the gradient of tf.gather), an IndexedSlices class is provided that encapsulates a set of sparse indices and values. IndexedSlices objects are detected and handled automatically by the optimizers in most cases.

tf.scatter_update(ref, indices, updates, use_locking=None, name=None)

Applies sparse updates to a variable reference.

This operation computes

# Scalar indices
ref[indices, ...] = updates[...]

# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value.

If values in ref is to be updated more than once, because there are duplicate entires in indices, the order at which the updates happen for each value is undefined.

Requires updates.shape = indices.shape + ref.shape[1:].

Args:
  • ref: A mutable Tensor. Should be from a Variable node.
  • indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  • updates: A Tensor. Must have the same type as ref. A tensor of updated values to store in ref.
  • use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  • name: A name for the operation (optional).
Returns:

Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done.

tf.scatter_add(ref, indices, updates, use_locking=None, name=None)

Adds sparse updates to a variable reference.

This operation computes

# Scalar indices
ref[indices, ...] += updates[...]

# Vector indices (for each i)
ref[indices[i], ...] += updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value.

Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add.

Requires updates.shape = indices.shape + ref.shape[1:].

Args:
  • ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node.
  • indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  • updates: A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
  • use_locking: An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  • name: A name for the operation (optional).
Returns:

Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done.

tf.scatter_sub(ref, indices, updates, use_locking=None, name=None)

Subtracts sparse updates to a variable reference.

# Scalar indices
ref[indices, ...] -= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value.

Duplicate entries are handled correctly: if multiple indices reference the same location, their (negated) contributions add.

Requires updates.shape = indices.shape + ref.shape[1:].

Args:
  • ref: A mutable Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. Should be from a Variable node.
  • indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.
  • updates: A Tensor. Must have the same type as ref. A tensor of updated values to subtract from ref.
  • use_locking: An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
  • name: A name for the operation (optional).
Returns:

Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done.

tf.sparse_mask(a, mask_indices, name=None)

Masks elements of IndexedSlices.

Given an IndexedSlices instance a, returns another IndexedSlices that contains a subset of the slices of a. Only the slices at indices specified in mask_indices are returned.

This is useful when you need to extract a subset of slices in an IndexedSlices object.

For example:

# `a` contains slices at indices [12, 26, 37, 45] from a large tensor
# with shape [1000, 10]
a.indices => [12, 26, 37, 45]
tf.shape(a.values) => [4, 10]

# `b` will be the subset of `a` slices at its second and third indices, so
# we want to mask of its first and last indices (which are at absolute
# indices 12, 45)
b = tf.sparse_mask(a, [12, 45])

b.indices => [26, 37]
tf.shape(b.values) => [2, 10]
Args:
  • a: An IndexedSlices instance.
  • mask_indices: Indices of elements to mask.
  • name: A name for the operation (optional).
Returns:

The masked IndexedSlices instance.

class tf.IndexedSlices

A sparse representation of a set of tensor slices at given indices.

This class is a simple wrapper for a pair of Tensor objects:

  • values: A Tensor of any dtype with shape [D0, D1, ..., Dn].
  • indices: A 1-D integer Tensor with shape [D0].

An IndexedSlices is typically used to represent a subset of a larger tensor dense of shape [LARGE0, D1, .. , DN] where LARGE0 >> D0. The values in indices are the indices in the first dimension of the slices that have been extracted from the larger tensor.

The dense tensor dense represented by an IndexedSlices slices has

dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]

The IndexedSlices class is used principally in the definition of gradients for operations that have sparse gradients (e.g. tf.gather).

Contrast this representation with SparseTensor, which uses multi-dimensional indices and scalar values.

tf.IndexedSlices.__init__(values, indices, dense_shape=None)

Creates an IndexedSlices.

tf.IndexedSlices.values

A Tensor containing the values of the slices.

tf.IndexedSlices.indices

A 1-D Tensor containing the indices of the slices.

tf.IndexedSlices.dense_shape

A 1-D Tensor containing the shape of the corresponding dense tensor.

tf.IndexedSlices.name

The name of this IndexedSlices.

tf.IndexedSlices.dtype

The DType of elements in this tensor.

tf.IndexedSlices.device

The name of the device on which values will be produced, or None.

tf.IndexedSlices.op

The Operation that produces values as an output.

Other Methods

tf.IndexedSlices.graph

The Graph that contains the values, indices, and shape tensors.

Exporting and Importing Meta Graphs

tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False)

Returns MetaGraphDef proto. Optionally writes it to filename.

This function exports the graph, saver, and collection objects into MetaGraphDef protocol buffer with the intension of it being imported at a later time or location to restart training, run inference, or be a subgraph.

Args:
  • filename: Optional filename including the path for writing the generated MetaGraphDef protocol buffer.
  • meta_info_def: MetaInfoDef protocol buffer.
  • graph_def: GraphDef protocol buffer.
  • saver_def: SaverDef protocol buffer.
  • collection_list: List of string keys to collect.
  • as_text: If True, writes the MetaGraphDef as an ASCII proto.
Returns:

A MetaGraphDef proto.

tf.train.import_meta_graph(meta_graph_or_file)

Recreates a Graph saved in a MetaGraphDef proto.

This function takes a MetaGraphDef protocol buffer as input. If the argument is a file containing a MetaGraphDef protocol buffer , it constructs a protocol buffer from the file content. The function then adds all the nodes from the graph_def field to the current graph, recreates all the collections, and returns a saver constructed from the saver_def field.

In combination with export_meta_graph(), this function can be used to

  • Serialize a graph along with other Python objects such as QueueRunner, Variable into a MetaGraphDef.

  • Restart training from a saved graph and checkpoints.

  • Run inference from a saved graph and checkpoints.

...
# Create a saver.
saver = tf.train.Saver(...variables...)
# Remember the training_op we want to run by adding it to a collection.
tf.add_to_collection('train_op', train_op)
sess = tf.Session()
for step in xrange(1000000):
    sess.run(train_op)
    if step % 1000 == 0:
        # Saves checkpoint, which by default also exports a meta_graph
        # named 'my-model-global_step.meta'.
        saver.save(sess, 'my-model', global_step=step)

Later we can continue training from this saved meta_graph without building the model from scratch.

with tf.Session() as sess:
  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
  new_saver.restore(sess, 'my-save-dir/my-model-10000')
  # tf.get_collection() returns a list. In this example we only want the
  # first one.
  train_op = tf.get_collection('train_op')[0]
  for step in xrange(1000000):
    sess.run(train_op)

NOTE: Restarting training from saved meta_graph only works if the device assignments have not changed.

Args:
  • meta_graph_or_file: MetaGraphDef protocol buffer or filename (including the path) containing a MetaGraphDef.
Returns:

A saver constructed from saver_def in MetaGraphDef or None.

A None value is returned if no variables exist in the MetaGraphDef (i.e., there are no variables to restore).

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