@tf_export("Variable") class Variable(checkpointable.CheckpointableBase): ? """See the @{$variables$Variables How To} for a high level overview.
? A variable maintains state in the graph across calls to `run()`. You add a??variable to the graph by constructing an instance of the class `Variable`.
? The `Variable()` constructor requires an initial value for the variable, which can be a `Tensor` of any type and shape. The initial value defines the??type and shape of the variable. After construction, the type and shape of ? the variable are fixed. The value can be changed using one of the assign??methods.
? If you want to change the shape of a variable later you have to use an??`assign` Op with `validate_shape=False`.
? Just like any `Tensor`, variables created with `Variable()` can be used as inputs for other Ops in the graph. Additionally, all the operators overloaded for the `Tensor` class are carried over to variables, so you can ? also add nodes to the graph by just doing arithmetic on variables.
? ```python ? 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)
When you launch the graph, variables have to be explicitly initialized before you can run Ops that use their value. You can initialize a variable by running its *initializer op*, restoring the variable from a save file, or simply running an `assign` Op that assigns a value to the variable. In fact,??the variable *initializer op* is just an `assign` Op that assigns the variable's initial value to the variable itself.
? ```python ? # 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'... ? ```
? The most common initialization pattern is to use the convenience function global_variables_initializer()` to add an Op to the graph that initializes??all the variables. You then run that Op after launching the graph.
? ```python ? # Add an Op to initialize global variables. ? init_op = tf.global_variables_initializer()
? # Launch the graph in a session. ? with tf.Session() as sess: ? ? ? # Run the Op that initializes global variables. ? ? ? sess.run(init_op) ? ? ? # ...you can now run any Op that uses variable values... ? ```
? If you need to create a variable with an initial value dependent on another variable, use the other variable's `initialized_value()`. This ensures that variables are initialized in the right order. All variables are automatically collected in the graph where they are created. By default, the constructor adds the new variable to the graph? collection `GraphKeys.GLOBAL_VARIABLES`. The convenience function
? `global_variables()` returns the contents of that collection.
? When building a machine learning model it is often convenient to distinguish??between variables holding the trainable model parameters and other variables??such as a `global step` variable used to count training steps. To make this??easier, the variable constructor supports a `trainable=<bool>` parameter. If `True`, the new variable is also added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. The convenience function `trainable_variables()` returns the contents of this collection. The various `Optimizer` classes use this collection as the default list of??variables to optimize.
? WARNING: tf.Variable objects have a non-intuitive memory model. A Variable is represented internally as a mutable Tensor which can non-deterministically alias other Tensors in a graph. The set of operations which consume a Variable??and can lead to aliasing is undetermined and can change across TensorFlow versions. Avoid writing code which relies on the value of a Variable either??changing or not changing as other operations happen. For example, using Variable objects or simple functions thereof as predicates in a `tf.cond` is??dangerous and error-prone:
? ``` ? v = tf.Variable(True) ? tf.cond(v, lambda: v.assign(False), my_false_fn) ?# Note: this is broken. ? ```
? Here replacing tf.Variable with tf.contrib.eager.Variable will fix any nondeterminism issues.
? To use the replacement for variables which does not have these issues:
? * Replace `tf.Variable` with `tf.contrib.eager.Variable`; ? * Call `tf.get_variable_scope().set_use_resource(True)` inside a??`tf.variable_scope` before the `tf.get_variable()` call.
? @compatibility(eager) ? `tf.Variable` is not compatible with eager execution. ?Use??`tf.contrib.eager.Variable` instead which is compatible with both eager??execution and graph construction. ?See [the TensorFlow Eager Execution??guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers) ? for details on how variables work in eager execution. ? @end_compatibility ? """
? 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.GLOBAL_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, referencing the variable's nodes ? ? ? ? in the graph, which must already exist. The graph is not changed. `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. ? ? ? expected_shape: A TensorShape. If set, initial_value is expected??to have this shape. ? ? ? import_scope: Optional `string`. Name scope to add to the?? `Variable.` Only used when initializing from protocol buffer. ? ? ? constraint: An optional projection function to be applied to the variable after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must??take as input the unprojected Tensor representing the value of the?? variable and return the Tensor for the projected value?? (which must have the same shape). Constraints are not safe to??use when doing asynchronous distributed training.
? ? 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`. ? ? ? RuntimeError: If eager execution is enabled.
? ? @compatibility(eager) ? ? `tf.Variable` is not compatible with eager execution. ?Use ? ? `tfe.Variable` instead which is compatible with both eager execution ? ? and graph construction. ?See [the TensorFlow Eager Execution ? ? guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers) ? ? for details on how variables work in eager execution. ? ? @end_compatibility
@tf_export("Variable")
class Variable(checkpointable.CheckpointableBase):"""See the @{$variables$Variables How To} for a high level overview.A variable maintains state in the graph across calls to `run()`. You add avariable to the graph by constructing an instance of the class `Variable`.The `Variable()` constructor requires an initial value for the variable,which can be a `Tensor` of any type and shape. The initial value defines thetype and shape of the variable. After construction, the type and shape ofthe variable are fixed. The value can be changed using one of the assignmethods.If you want to change the shape of a variable later you have to use an`assign` Op with `validate_shape=False`.Just like any `Tensor`, variables created with `Variable()` can be used asinputs for other Ops in the graph. Additionally, all the operatorsoverloaded for the `Tensor` class are carried over to variables, so you canalso add nodes to the graph by just doing arithmetic on variables.```pythonimport 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)```When you launch the graph, variables have to be explicitly initialized beforeyou can run Ops that use their value. You can initialize a variable byrunning its *initializer op*, restoring the variable from a save file, orsimply running an `assign` Op that assigns a value to the variable. In fact,the variable *initializer op* is just an `assign` Op that assigns thevariable's initial value to the variable itself.```python# 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'...```The most common initialization pattern is to use the convenience function`global_variables_initializer()` to add an Op to the graph that initializesall the variables. You then run that Op after launching the graph.```python# Add an Op to initialize global variables.init_op = tf.global_variables_initializer()# Launch the graph in a session.with tf.Session() as sess:# Run the Op that initializes global variables.sess.run(init_op)# ...you can now run any Op that uses variable values...```If you need to create a variable with an initial value dependent on anothervariable, use the other variable's `initialized_value()`. This ensures thatvariables are initialized in the right order.All variables are automatically collected in the graph where they arecreated. By default, the constructor adds the new variable to the graphcollection `GraphKeys.GLOBAL_VARIABLES`. The convenience function`global_variables()` returns the contents of that collection.When building a machine learning model it is often convenient to distinguishbetween variables holding the trainable model parameters and other variablessuch as a `global step` variable used to count training steps. To make thiseasier, the variable constructor supports a `trainable=<bool>` parameter. If`True`, the new variable is also added to the graph collection`GraphKeys.TRAINABLE_VARIABLES`. The convenience function`trainable_variables()` returns the contents of this collection. Thevarious `Optimizer` classes use this collection as the default list ofvariables to optimize.WARNING: tf.Variable objects have a non-intuitive memory model. A Variable isrepresented internally as a mutable Tensor which can non-deterministicallyalias other Tensors in a graph. The set of operations which consume a Variableand can lead to aliasing is undetermined and can change across TensorFlowversions. Avoid writing code which relies on the value of a Variable eitherchanging or not changing as other operations happen. For example, usingVariable objects or simple functions thereof as predicates in a `tf.cond` isdangerous and error-prone:```v = tf.Variable(True)tf.cond(v, lambda: v.assign(False), my_false_fn) # Note: this is broken.```Here replacing tf.Variable with tf.contrib.eager.Variable will fix anynondeterminism issues.To use the replacement for variables which doesnot have these issues:* Replace `tf.Variable` with `tf.contrib.eager.Variable`;* Call `tf.get_variable_scope().set_use_resource(True)` inside a`tf.variable_scope` before the `tf.get_variable()` call.@compatibility(eager)`tf.Variable` is not compatible with eager execution. Use`tf.contrib.eager.Variable` instead which is compatible with both eagerexecution and graph construction. See [the TensorFlow Eager Executionguide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)for details on how variables work in eager execution.@end_compatibility"""def __init__(self,initial_value=None,trainable=True,collections=None,validate_shape=True,caching_device=None,name=None,variable_def=None,dtype=None,expected_shape=None,import_scope=None,constraint=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.GLOBAL_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 thevariable 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 havea shape specified unless `validate_shape` is set to False. Can also be acallable with no argument that returns the initial value when called. Inthat case, `dtype` must be specified. (Note that initializer functionsfrom 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 graphcollection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used asthe default list of variables to use by the `Optimizer` classes.collections: List of graph collections keys. The new variable is added tothese collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.validate_shape: If `False`, allows the variable to be initialized with avalue of unknown shape. If `True`, the default, the shape of`initial_value` must be known.caching_device: Optional device string describing where the Variableshould be cached for reading. Defaults to the Variable's device.If not `None`, caches on another device. Typical use is to cacheon the device where the Ops using the Variable reside, to deduplicatecopying through `Switch` and other conditional statements.name: Optional name for the variable. Defaults to `'Variable'` and getsuniquified automatically.variable_def: `VariableDef` protocol buffer. If not `None`, recreatesthe Variable object with its contents, referencing the variable's nodesin the graph, which must already exist. The graph is not changed.`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` isa Tensor), or `convert_to_tensor` will decide.expected_shape: A TensorShape. If set, initial_value is expectedto have this shape.import_scope: Optional `string`. Name scope to add to the`Variable.` Only used when initializing from protocol buffer.constraint: An optional projection function to be applied to the variableafter being updated by an `Optimizer` (e.g. used to implement normconstraints or value constraints for layer weights). The function musttake as input the unprojected Tensor representing the value of thevariable and return the Tensor for the projected value(which must have the same shape). Constraints are not safe touse when doing asynchronous distributed training.Raises:ValueError: If both `variable_def` and initial_value are specified.ValueError: If the initial value is not specified, or does not have ashape and `validate_shape` is `True`.RuntimeError: If eager execution is enabled.@compatibility(eager)`tf.Variable` is not compatible with eager execution. Use`tfe.Variable` instead which is compatible with both eager executionand graph construction. See [the TensorFlow Eager Executionguide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers)for details on how variables work in eager execution.@end_compatibility
?
?
tensorflow.get_variable()函數(shù)
# The argument list for get_variable must match arguments to get_local_variable.
# So, if you are updating the arguments, also update arguments to
# get_local_variable below.
@tf_export("get_variable")
def get_variable(name,shape=None,dtype=None,initializer=None,regularizer=None,trainable=None,collections=None,caching_device=None,partitioner=None,validate_shape=True,use_resource=None,custom_getter=None,constraint=None,synchronization=VariableSynchronization.AUTO,aggregation=VariableAggregation.NONE):return get_variable_scope().get_variable(_get_default_variable_store(),name,shape=shape,dtype=dtype,initializer=initializer,regularizer=regularizer,trainable=trainable,collections=collections,caching_device=caching_device,partitioner=partitioner,validate_shape=validate_shape,use_resource=use_resource,custom_getter=custom_getter,constraint=constraint,synchronization=synchronization,aggregation=aggregation)
