# Variables

### class tf.Variable

See the 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.

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)


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.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.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.

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, expected_shape=None, import_scope=None) {:#Variable.init}

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 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.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. 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.
##### 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.global_variables_initializer()

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.

#### tf.Variable.__abs__(a, *args) {:#Variable.abs}

Computes the absolute value of a tensor.

Given a tensor of real numbers x, this operation returns a tensor containing the absolute value of each element in x. For example, if x is an input element and y is an output element, this operation computes $$y = |x|$$.

See tf.complex_abs() to compute the absolute value of a complex number.

##### Args:
• x: A Tensor or SparseTensor of type float32, float64, int32, or int64.
• name: A name for the operation (optional).
##### Returns:

A Tensor or SparseTensor the same size and type as x with absolute values.

#### tf.Variable.__add__(a, *args) {:#Variable.add}

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__and__(a, *args) {:#Variable.and}

Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__div__(a, *args) {:#Variable.div}

Returns x / y element-wise.

NOTE: Div supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__floordiv__(a, *args) {:#Variable.floordiv}

Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

##### Args:
• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).
##### Returns:

x / y rounded down (except possibly towards zero for negative integers).

##### Raises:
• TypeError: If the inputs are complex.

#### tf.Variable.__ge__(a, *args) {:#Variable.ge}

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

NOTE: GreaterEqual supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__getitem__(var, slice_spec) {:#Variable.getitem}

Creates a slice helper object given a variable.

This allows creating a sub-tensor from part of the current contents of a variable. See 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(). For example,

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

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


Note that assignments currently do not support NumPy broadcasting semantics.

##### Args:
• var: An ops.Variable object.
• slice_spec: The arguments to Tensor.__getitem__.
##### Returns:

The appropriate slice of "tensor", based on "slice_spec". As an operator. The operator also has a assign() method that can be used to generate an assignment operator.

##### Raises:
• ValueError: If a slice range is negative size.
• TypeError: If the slice indices aren't int, slice, or Ellipsis.

#### tf.Variable.__gt__(a, *args) {:#Variable.gt}

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

NOTE: Greater supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__invert__(a, *args) {:#Variable.invert}

Returns the truth value of NOT x element-wise.

##### Args:
• x: A Tensor of type bool.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__iter__() {:#Variable.iter}

Dummy method to prevent iteration. Do not call.

NOTE(mrry): If we register getitem as an overloaded operator, Python will valiantly attempt to iterate over the variable's Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

##### Raises:
• TypeError: when invoked.

#### tf.Variable.__le__(a, *args) {:#Variable.le}

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

NOTE: LessEqual supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__lt__(a, *args) {:#Variable.lt}

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

NOTE: Less supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__mod__(a, *args) {:#Variable.mod}

Returns element-wise remainder of division.

NOTE: Mod supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: int32, int64, float32, float64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__mul__(a, *args) {:#Variable.mul}

Dispatches cwise mul for "DenseDense" and "DenseSparse".

#### tf.Variable.__neg__(a, *args) {:#Variable.neg}

Computes numerical negative value element-wise.

I.e., $$y = -x$$.

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__or__(a, *args) {:#Variable.or}

Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__pow__(a, *args) {:#Variable.pow}

Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes $$x^y$$ for corresponding elements in x and y. For example:

# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]

##### Args:
• x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• name: A name for the operation (optional).
##### Returns:

A Tensor.

#### tf.Variable.__radd__(a, *args) {:#Variable.radd}

Returns x + y element-wise.

NOTE: Add supports broadcasting. AddN does not. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__rand__(a, *args) {:#Variable.rand}

Returns the truth value of x AND y element-wise.

NOTE: LogicalAnd supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__rdiv__(a, *args) {:#Variable.rdiv}

Returns x / y element-wise.

NOTE: Div supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__rfloordiv__(a, *args) {:#Variable.rfloordiv}

Divides x / y elementwise, rounding toward the most negative integer.

The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division.

Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Numpy).

x and y must have the same type, and the result will have the same type as well.

##### Args:
• x: Tensor numerator of real numeric type.
• y: Tensor denominator of real numeric type.
• name: A name for the operation (optional).
##### Returns:

x / y rounded down (except possibly towards zero for negative integers).

##### Raises:
• TypeError: If the inputs are complex.

#### tf.Variable.__rmod__(a, *args) {:#Variable.rmod}

Returns element-wise remainder of division.

NOTE: Mod supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: int32, int64, float32, float64.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__rmul__(a, *args) {:#Variable.rmul}

Dispatches cwise mul for "DenseDense" and "DenseSparse".

#### tf.Variable.__ror__(a, *args) {:#Variable.ror}

Returns the truth value of x OR y element-wise.

NOTE: LogicalOr supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor of type bool.
• y: A Tensor of type bool.
• name: A name for the operation (optional).
##### Returns:

A Tensor of type bool.

#### tf.Variable.__rpow__(a, *args) {:#Variable.rpow}

Computes the power of one value to another.

Given a tensor x and a tensor y, this operation computes $$x^y$$ for corresponding elements in x and y. For example:

# tensor 'x' is [[2, 2], [3, 3]]
# tensor 'y' is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]

##### Args:
• x: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• y: A Tensor of type float32, float64, int32, int64, complex64, or complex128.
• name: A name for the operation (optional).
##### Returns:

A Tensor.

#### tf.Variable.__rsub__(a, *args) {:#Variable.rsub}

Returns x - y element-wise.

NOTE: Sub supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__rtruediv__(a, *args) {:#Variable.rtruediv}

Divides x / y elementwise, always producing floating point results.

The same as tf.div for floating point arguments, but casts integer arguments to floating point before dividing so that the result is always floating point. This op is generated by normal x / y division in Python 3 and in Python 2.7 with from __future__ import division. If you want integer division that rounds down, use x // y or tf.floordiv.

x and y must have the same numeric type. If the inputs are floating point, the output will have the same type. If the inputs are integral, the inputs are cast to float32 for int8 and int16 and float64 for int32 and int64 (matching the behavior of Numpy).

##### Args:
• x: Tensor numerator of numeric type.
• y: Tensor denominator of numeric type.
• name: A name for the operation (optional).
##### Returns:

x / y evaluated in floating point.

##### Raises:
• TypeError: If x and y have different dtypes.

#### tf.Variable.__rxor__(a, *args) {:#Variable.rxor}

x ^ y = (x | y) & ~(x & y).

#### tf.Variable.__sub__(a, *args) {:#Variable.sub}

Returns x - y element-wise.

NOTE: Sub supports broadcasting. More about broadcasting here

##### Args:
• x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128.
• y: A Tensor. Must have the same type as x.
• name: A name for the operation (optional).
##### Returns:

A Tensor. Has the same type as x.

#### tf.Variable.__truediv__(a, *args) {:#Variable.truediv}

Divides x / y elementwise, always producing floating point results.

The same as tf.div for floating point arguments, but casts integer arguments to floating point before dividing so that the result is always floating point. This op is generated by normal x / y division in Python 3 and in Python 2.7 with from __future__ import division. If you want integer division that rounds down, use x // y or tf.floordiv.

x and y must have the same numeric type. If the inputs are floating point, the output will have the same type. If the inputs are integral, the inputs are cast to float32 for int8 and int16 and float64 for int32 and int64 (matching the behavior of Numpy).

##### Args:
• x: Tensor numerator of numeric type.
• y: Tensor denominator of numeric type.
• name: A name for the operation (optional).
##### Returns:

x / y evaluated in floating point.

##### Raises:
• TypeError: If x and y have different dtypes.

#### tf.Variable.__xor__(a, *args) {:#Variable.xor}

x ^ y = (x | y) & ~(x & y).

#### tf.Variable.from_proto(variable_def, import_scope=None)

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.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.

##### Returns:

A Tensor containing the value of the variable.

#### tf.Variable.set_shape(shape)

Overrides the shape for this variable.

##### Args:
• shape: the TensorShape representing the overridden shape.

#### tf.Variable.to_proto(export_scope=None)

Converts a Variable to a VariableDef protocol buffer.

##### Args:
• export_scope: Optional string. Name scope to remove.
##### Returns:

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

#### 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.