View source on GitHub
|
Variable based on resource handles.
tf.contrib.eager.Variable(
initial_value=None, trainable=None, collections=None, validate_shape=True,
caching_device=None, name=None, dtype=None, variable_def=None,
import_scope=None, constraint=None, distribute_strategy=None,
synchronization=None, aggregation=None, shape=None
)
See the Variables How To for a high level overview.
A ResourceVariable allows you to maintain state across subsequent calls to
session.run.
The ResourceVariable 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.
Just like any Tensor, variables created with
tf.Variable(use_resource=True) 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.
Unlike ref-based variable, a ResourceVariable has well-defined semantics. Each usage of a ResourceVariable in a TensorFlow graph adds a read_value operation to the graph. The Tensors returned by a read_value operation are guaranteed to see all modifications to the value of the variable which happen in any operation on which the read_value depends on (either directly, indirectly, or via a control dependency) and guaranteed to not see any modification to the value of the variable from operations that depend on the read_value operation. Updates from operations that have no dependency relationship to the read_value operation might or might not be visible to read_value.
For example, if there is more than one assignment to a ResourceVariable in a single session.run call there is a well-defined value for each operation which uses the variable's value if the assignments and the read are connected by edges in the graph. Consider the following example, in which two writes can cause tf.Variable and tf.ResourceVariable to behave differently:
a = tf.Variable(1.0, use_resource=True)
a.initializer.run()
assign = a.assign(2.0)
with tf.control_dependencies([assign]):
b = a.read_value()
with tf.control_dependencies([b]):
other_assign = a.assign(3.0)
with tf.control_dependencies([other_assign]):
# Will print 2.0 because the value was read before other_assign ran. If
# `a` was a tf.Variable instead, 2.0 or 3.0 could be printed.
tf.compat.v1.Print(b, [b]).eval()
Args | |
|---|---|
initial_value
|
A Tensor, or Python object convertible to a Tensor,
which is the initial value for the Variable. Can also be a
callable with no argument that returns the initial value when called.
(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.
Defaults to True, unless synchronization is set to ON_READ, in
which case it defaults to False.
|
collections
|
List of graph collections keys. The new variable is added to
these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES].
|
validate_shape
|
Ignored. Provided for compatibility with 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.
|
name
|
Optional name for the variable. Defaults to 'Variable' and gets
uniquified automatically.
|
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 float32 will be used (if it is a Python object convertible to a Tensor). |
variable_def
|
VariableDef protocol buffer. If not None, recreates the
ResourceVariable object with its contents. variable_def and other
arguments (except for import_scope) are mutually exclusive.
|
import_scope
|
Optional string. Name scope to add to the
ResourceVariable. Only used when variable_def is provided.
|
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.
|
distribute_strategy
|
The tf.distribute.Strategy this variable is being created inside of. |
synchronization
|
Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
tf.VariableSynchronization. By default the synchronization is set to
AUTO and the current DistributionStrategy chooses
when to synchronize.
|
aggregation
|
Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
tf.VariableAggregation.
|
shape
|
(optional) The shape of this variable. If None, the shape of
initial_value will be used. When setting this argument to
tf.TensorShape(None) (representing an unspecified shape), the variable
can be assigned with values of different shapes.
|
Raises | |
|---|---|
ValueError
|
If the initial value is not specified, or does not have a
shape and validate_shape is True.
|
Attributes | |
|---|---|
aggregation
|
|
constraint
|
Returns the constraint function associated with this variable. |
create
|
The op responsible for initializing this variable. |
device
|
The device this variable is on. |
dtype
|
The dtype of this variable. |
graph
|
The Graph of this variable.
|
handle
|
The handle by which this variable can be accessed. |
initial_value
|
Returns the Tensor used as the initial value for the variable. |
initializer
|
The op responsible for initializing this variable. |
name
|
The name of the handle for this variable. |
op
|
The op for this variable. |
shape
|
The shape of this variable. |
synchronization
|
|
trainable
|
|
Child Classes
Methods
assign
assign(
value, use_locking=None, name=None, read_value=True
)
Assigns a new value to this variable.
| Args | |
|---|---|
value
|
A Tensor. The new value for this variable.
|
use_locking
|
If True, use locking during the assignment.
|
name
|
The name to use for the assignment. |
read_value
|
A bool. Whether to read and return the new value of the
variable or not.
|
| Returns | |
|---|---|
If read_value is True, this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None.
|
assign_add
assign_add(
delta, use_locking=None, name=None, read_value=True
)
Adds a value to this variable.
| Args | |
|---|---|
delta
|
A Tensor. The value to add to this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
The name to use for the operation. |
read_value
|
A bool. Whether to read and return the new value of the
variable or not.
|
| Returns | |
|---|---|
If read_value is True, this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None.
|
assign_sub
assign_sub(
delta, use_locking=None, name=None, read_value=True
)
Subtracts a value from this variable.
| Args | |
|---|---|
delta
|
A Tensor. The value to subtract from this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
The name to use for the operation. |
read_value
|
A bool. Whether to read and return the new value of the
variable or not.
|
| Returns | |
|---|---|
If read_value is True, this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None.
|
batch_scatter_update
batch_scatter_update(
sparse_delta, use_locking=False, name=None
)
Assigns tf.IndexedSlices to this variable batch-wise.
Analogous to batch_gather. This assumes that this variable and the
sparse_delta IndexedSlices have a series of leading dimensions that are the
same for all of them, and the updates are performed on the last dimension of
indices. In other words, the dimensions should be the following:
num_prefix_dims = sparse_delta.indices.ndims - 1
batch_dim = num_prefix_dims + 1
sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[
batch_dim:]
where
sparse_delta.updates.shape[:num_prefix_dims]
== sparse_delta.indices.shape[:num_prefix_dims]
== var.shape[:num_prefix_dims]
And the operation performed can be expressed as:
var[i_1, ..., i_n,
sparse_delta.indices[i_1, ..., i_n, j]] = sparse_delta.updates[
i_1, ..., i_n, j]
When sparse_delta.indices is a 1D tensor, this operation is equivalent to
scatter_update.
To avoid this operation one can looping over the first ndims of the
variable and using scatter_update on the subtensors that result of slicing
the first dimension. This is a valid option for ndims = 1, but less
efficient than this implementation.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to be assigned to this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
count_up_to
count_up_to(
limit
)
Increments this variable until it reaches limit. (deprecated)
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.
|
eval
eval(
session=None
)
Evaluates and returns the value of this variable.
experimental_ref
experimental_ref()
Returns a hashable reference object to this Variable.
The primary usecase for this API is to put variables in a set/dictionary.
We can't put variables in a set/dictionary as variable.__hash__() is no
longer available starting Tensorflow 2.0.
import tensorflow as tf
x = tf.Variable(5)
y = tf.Variable(10)
z = tf.Variable(10)
# The followings will raise an exception starting 2.0
# TypeError: Variable is unhashable if Variable equality is enabled.
variable_set = {x, y, z}
variable_dict = {x: 'five', y: 'ten'}
Instead, we can use variable.experimental_ref().
variable_set = {x.experimental_ref(),
y.experimental_ref(),
z.experimental_ref()}
print(x.experimental_ref() in variable_set)
==> True
variable_dict = {x.experimental_ref(): 'five',
y.experimental_ref(): 'ten',
z.experimental_ref(): 'ten'}
print(variable_dict[y.experimental_ref()])
==> ten
Also, the reference object provides .deref() function that returns the
original Variable.
x = tf.Variable(5)
print(x.experimental_ref().deref())
==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
from_proto
@staticmethodfrom_proto( variable_def, import_scope=None )
Returns a Variable object created from variable_def.
gather_nd
gather_nd(
indices, name=None
)
Reads the value of this variable sparsely, using gather_nd.
get_shape
get_shape()
Alias of Variable.shape.
initialized_value
initialized_value()
Returns the value of the initialized variable. (deprecated)
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.random.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.
|
is_initialized
is_initialized(
name=None
)
Checks whether a resource variable has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
| Args | |
|---|---|
name
|
A name for the operation (optional). |
| Returns | |
|---|---|
A Tensor of type bool.
|
load
load(
value, session=None
)
Load new value into this variable. (deprecated)
Writes new value to variable's memory. Doesn't 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 tf.compat.v1.Session for more
information on launching a graph and on sessions.
v = tf.Variable([1, 2])
init = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init)
# Usage passing the session explicitly.
v.load([2, 3], sess)
print(v.eval(sess)) # prints [2 3]
# Usage with the default session. The 'with' block
# above makes 'sess' the default session.
v.load([3, 4], sess)
print(v.eval()) # prints [3 4]
| Args | |
|---|---|
value
|
New variable value |
session
|
The session to use to evaluate this variable. If none, the default session is used. |
| Raises | |
|---|---|
ValueError
|
Session is not passed and no default session |
numpy
numpy()
read_value
read_value()
Constructs an op which reads the value of this variable.
Should be used when there are multiple reads, or when it is desirable to read the value only after some condition is true.
| Returns | |
|---|---|
| the read operation. |
scatter_add
scatter_add(
sparse_delta, use_locking=False, name=None
)
Adds tf.IndexedSlices to this variable.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to be added to this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered addition has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_div
scatter_div(
sparse_delta, use_locking=False, name=None
)
Divide this variable by tf.IndexedSlices.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to divide this variable by.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered division has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_max
scatter_max(
sparse_delta, use_locking=False, name=None
)
Updates this variable with the max of tf.IndexedSlices and itself.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to use as an argument of max
with this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered maximization has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_min
scatter_min(
sparse_delta, use_locking=False, name=None
)
Updates this variable with the min of tf.IndexedSlices and itself.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to use as an argument of min
with this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered minimization has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_mul
scatter_mul(
sparse_delta, use_locking=False, name=None
)
Multiply this variable by tf.IndexedSlices.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to multiply this variable by.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered multiplication has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_nd_add
scatter_nd_add(
indices, updates, name=None
)
Applies sparse addition to individual values or slices in a Variable.
ref is a Tensor with rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into ref.
It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to
indices into elements (if K = P) or slices (if K < P) along the Kth
dimension of ref.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
add = ref.scatter_nd_add(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(add)
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See tf.scatter_nd for more details about how to make updates to
slices.
| Args | |
|---|---|
indices
|
The indices to be used in the operation. |
updates
|
The values to be used in the operation. |
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
scatter_nd_sub
scatter_nd_sub(
indices, updates, name=None
)
Applies sparse subtraction to individual values or slices in a Variable.
ref is a Tensor with rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into ref.
It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to
indices into elements (if K = P) or slices (if K < P) along the Kth
dimension of ref.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_sub(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 6, 7, -4]
See tf.scatter_nd for more details about how to make updates to
slices.
| Args | |
|---|---|
indices
|
The indices to be used in the operation. |
updates
|
The values to be used in the operation. |
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
scatter_nd_update
scatter_nd_update(
indices, updates, name=None
)
Applies sparse assignment to individual values or slices in a Variable.
ref is a Tensor with rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into ref.
It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to
indices into elements (if K = P) or slices (if K < P) along the Kth
dimension of ref.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_update(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See tf.scatter_nd for more details about how to make updates to
slices.
| Args | |
|---|---|
indices
|
The indices to be used in the operation. |
updates
|
The values to be used in the operation. |
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
scatter_sub
scatter_sub(
sparse_delta, use_locking=False, name=None
)
Subtracts tf.IndexedSlices from this variable.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to be subtracted from this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
scatter_update
scatter_update(
sparse_delta, use_locking=False, name=None
)
Assigns tf.IndexedSlices to this variable.
| Args | |
|---|---|
sparse_delta
|
tf.IndexedSlices to be assigned to this variable.
|
use_locking
|
If True, use locking during the operation.
|
name
|
the name of the operation. |
| Returns | |
|---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
| Raises | |
|---|---|
TypeError
|
if sparse_delta is not an IndexedSlices.
|
set_shape
set_shape(
shape
)
Unsupported.
sparse_read
sparse_read(
indices, name=None
)
Reads the value of this variable sparsely, using gather.
to_proto
to_proto(
export_scope=None
)
Converts a ResourceVariable to a VariableDef protocol buffer.
| Args | |
|---|---|
export_scope
|
Optional string. Name scope to remove.
|
| Raises | |
|---|---|
RuntimeError
|
If run in EAGER mode. |
| Returns | |
|---|---|
A VariableDef protocol buffer, or None if the Variable is not
in the specified name scope.
|
value
value()
A cached operation which reads the value of this variable.
__abs__
__abs__(
x, name=None
)
Computes the absolute value of a tensor.
Given a tensor of integer or floating-point values, this operation returns a tensor of the same type, where each element contains the absolute value of the corresponding element in the input.
Given a tensor x of complex numbers, this operation returns a tensor of type
float32 or float64 that is the absolute value of each element in x. All
elements in x must be complex numbers of the form \(a + bj\). The
absolute value is computed as \( \sqrt{a^2 + b^2}\). For example:
x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x) # [5.25594902, 6.60492229]
| Args | |
|---|---|
x
|
A Tensor or SparseTensor of type float16, float32, float64,
int32, |