tf.compat.v1.Variable

See the Variables Guide.

Inherits From: Variable

tf.compat.v1.Variable(
    initial_value=None,
    trainable=None,
    collections=None,
    validate_shape=True,
    caching_device=None,
    name=None,
    variable_def=None,
    dtype=None,
    expected_shape=None,
    import_scope=None,
    constraint=None,
    use_resource=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.compat.v1.VariableAggregation.NONE,
    shape=None
)

Used in the notebooks

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.

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.

# Launch the graph in a session.
with tf.compat.v1.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.

# Add an Op to initialize global variables.
init_op = tf.compat.v1.global_variables_initializer()

# Launch the graph in a session.
with tf.compat.v1.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.

v = tf.Variable(True)
tf.cond(v, lambda: v.assign(False), my_false_fn)  # Note: this is broken.

Here, adding use_resource=True when constructing the variable will fix any nondeterminism issues:

v = tf.Variable(True, use_resource=True)
tf.cond(v, lambda: v.assign(False), my_false_fn)

To use the replacement for variables which does not have these issues:

• Add use_resource=True when constructing tf.Variable;
• Call tf.compat.v1.get_variable_scope().set_use_resource(True) inside a tf.compat.v1.variable_scope before the tf.compat.v1.get_variable() call.

Child Classes

class SaveSliceInfo

Methods

assign

View source

assign(
    value, use_locking=False, name=None, read_value=True
)

Assigns a new value to the variable.

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

assign_add

View source

assign_add(
    delta, use_locking=False, name=None, read_value=True
)

Adds a value to this variable.

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

assign_sub

View source

assign_sub(
    delta, use_locking=False, name=None, read_value=True
)

Subtracts a value from this variable.

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

batch_scatter_update

View source

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.

count_up_to

View source

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).

eval

View source

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 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.
    print(v.eval(sess))
    # Usage with the default session.  The 'with' block
    # above makes 'sess' the default session.
    print(v.eval())

experimental_ref

View source

experimental_ref()

DEPRECATED FUNCTION

from_proto

View source

@staticmethod
from_proto(
    variable_def, import_scope=None
)

Returns a Variable object created from variable_def.

gather_nd

View source

gather_nd(
    indices, name=None
)

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

get_shape

View source

get_shape() -> tf.TensorShape

Alias of Variable.shape.

initialized_value

View source

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)

load

View source

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]

read_value

View source

read_value()

Returns the value of this variable, read in the current context.

Can be different from value() if it's on another device, with control dependencies, etc.

ref

View source

ref()

Returns a hashable reference object to this Variable.

The primary use case 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.

The following will raise an exception starting 2.0

x = tf.Variable(5)
y = tf.Variable(10)
z = tf.Variable(10)
variable_set = {x, y, z}
Traceback (most recent call last):

TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):

TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.

Instead, we can use variable.ref().

variable_set = {x.ref(), y.ref(), z.ref()}
x.ref() in variable_set
True
variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
variable_dict[y.ref()]
'ten'

Also, the reference object provides .deref() function that returns the original Variable.

x = tf.Variable(5)
x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

scatter_add

View source

scatter_add(
    sparse_delta, use_locking=False, name=None
)

Adds tf.IndexedSlices to this variable.

scatter_div

View source

scatter_div(
    sparse_delta, use_locking=False, name=None
)

Divide this variable by tf.IndexedSlices.

scatter_max

View source

scatter_max(
    sparse_delta, use_locking=False, name=None
)

Updates this variable with the max of tf.IndexedSlices and itself.

scatter_min

View source

scatter_min(
    sparse_delta, use_locking=False, name=None
)

Updates this variable with the min of tf.IndexedSlices and itself.

scatter_mul

View source

scatter_mul(
    sparse_delta, use_locking=False, name=None
)

Multiply this variable by tf.IndexedSlices.

scatter_nd_add

View source

scatter_nd_add(
    indices, updates, name=None
)

Applies sparse addition to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. 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 self.

updates is Tensor of rank Q-1+P-K with shape:

[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_add(indices, updates)
print(v)

The resulting update to v 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.

scatter_nd_sub

View source

scatter_nd_sub(
    indices, updates, name=None
)

Applies sparse subtraction to individual values or slices in a Variable.

Assuming the variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. 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 self.

updates is Tensor of rank Q-1+P-K with shape:

[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_sub(indices, updates)
print(v)

After the update v would look like this:

[1, -9, 3, -6, -4, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

scatter_nd_update

View source

scatter_nd_update(
    indices, updates, name=None
)

Applies sparse assignment to individual values or slices in a Variable.

The Variable has rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into self. 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 self.

updates is Tensor of rank Q-1+P-K with shape:

[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:

v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_update(indices, updates)
print(v)

The resulting update to v 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.

scatter_sub

View source

scatter_sub(
    sparse_delta, use_locking=False, name=None
)

Subtracts tf.IndexedSlices from this variable.

scatter_update

View source

scatter_update(
    sparse_delta, use_locking=False, name=None
)

Assigns tf.IndexedSlices to this variable.

set_shape

View source

set_shape(
    shape
)

Overrides the shape for this variable.

sparse_read

View source

sparse_read(
    indices, name=None
)

Gather slices from params axis axis according to indices.

This function supports a subset of tf.gather, see tf.gather for details on usage.

to_proto

View source

to_proto(
    export_scope=None
)

Converts a Variable to a VariableDef protocol buffer.

value

View source

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.

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.

__abs__

View source

__abs__(
    name=None
)

__add__

View source

__add__(
    y
)

__and__

View source

__and__(
    y
)

__div__

View source

__div__(
    y
)

__eq__

View source

__eq__(
    other
)

Compares two variables element-wise for equality.

__floordiv__

View source

__floordiv__(
    y
)

__ge__

__ge__(
    y: Annotated[Any, tf.raw_ops.Any],
    name=None
) -> Annotated[Any, tf.raw_ops.Any]

Returns the truth value of (x >= y) element-wise.

Note: math.greater_equal supports broadcasting. More about broadcasting here

Example:

x = tf.constant([5, 4, 6, 7])
y = tf.constant([5, 2, 5, 10])
tf.math.greater_equal(x, y) ==> [True, True, True, False]

x = tf.constant([5, 4, 6, 7])
y = tf.constant([5])
tf.math.greater_equal(x, y) ==> [True, False, True, True]

__getitem__

View source

__getitem__(
    slice_spec
)

Creates a slice helper object given a variable.

This allows creating a sub-tensor from part of the current contents of a variable. See tf.Tensor.getitem for detailed examples of slicing.

This function in addition also allows assignment to a sliced range. This is similar to __setitem__ functionality in Python. However, the syntax is different so that the user can capture the assignment operation for grouping or passing to sess.run() in TF1. For example,

import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
print(A[:2, :2])  # => [[1,2], [4,5]]

A[:2,:2].assign(22. * tf.ones((2, 2))))
print(A) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]

Note that assignments currently do not support NumPy broadcasting semantics.

__gt__

__gt__(
    y: Annotated[Any, tf.raw_ops.Any],
    name=None
) -> Annotated[Any, tf.raw_ops.Any]

Returns the truth value of (x > y) element-wise.

Note: math.greater supports broadcasting. More about broadcasting here

Example:

x = tf.constant([5, 4, 6])
y = tf.constant([5, 2, 5])
tf.math.greater(x, y) ==> [False, True, True]

x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.greater(x, y) ==> [False, False, True]

__invert__

View source

__invert__(
    name=None
)

__iter__

View source

__iter__()

When executing eagerly, iterates over the value of the variable.

__le__

__le__(
    y: Annotated[Any, tf.raw_ops.Any],
    name=None
) -> Annotated[Any, tf.raw_ops.Any]

Returns the truth value of (x <= y) element-wise.

Note: math.less_equal supports broadcasting. More about broadcasting here

Example:

x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less_equal(x, y) ==> [True, True, False]

x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 6])
tf.math.less_equal(x, y) ==> [True, True, True]

__lt__

__lt__(
    y: Annotated[Any, tf.raw_ops.Any],
    name=None
) -> Annotated[Any, tf.raw_ops.Any]

Returns the truth value of (x < y) element-wise.

Note: math.less supports broadcasting. More about broadcasting here

Example:

x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less(x, y) ==> [False, True, False]

x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 7])
tf.math.less(x, y) ==> [False, True, True]

__matmul__

View source

__matmul__(
    y
)

__mod__

View source

__mod__(
    y
)

__mul__

View source

__mul__(
    y
)

__ne__

View source

__ne__(
    other
)

Compares two variables element-wise for equality.

__neg__

__neg__(
    name=None
) -> Annotated[Any, tf.raw_ops.Any]

Computes numerical negative value element-wise.

I.e., \(y = -x\).

__or__

View source

__or__(
    y
)

__pow__

View source

__pow__(
    y
)

__radd__

View source

__radd__(
    x
)

__rand__

View source

__rand__(
    x
)

__rdiv__

View source

__rdiv__(
    x
)

__rfloordiv__

View source

__rfloordiv__(
    x
)

__rmatmul__

View source

__rmatmul__(
    x
)

__rmod__

View source

__rmod__(
    x
)

__rmul__

View source

__rmul__(
    x
)

__ror__

View source

__ror__(
    x
)

__rpow__

View source

__rpow__(
    x
)

__rsub__

View source

__rsub__(
    x
)

__rtruediv__

View source

__rtruediv__(
    x
)

__rxor__

View source

__rxor__(
    x
)

__sub__

View source

__sub__(
    y
)

__truediv__

View source

__truediv__(
    y
)

__xor__

View source

__xor__(
    y
)