Variable
Defined in tensorflow/python/ops/variables.py
.
See the guide: Variables > Variables
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) 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.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.
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 for details on how variables work in eager execution.
constraint
Returns the constraint function associated with this variable.
The constraint function that was passed to the variable constructor. Can be None
if no constraint was passed.
device
The device of this variable.
dtype
The DType
of this variable.
graph
The Graph
of this 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.
A Tensor
.
initializer
The initializer operation for this variable.
name
The name of this variable.
op
The Operation
of this variable.
shape
The TensorShape
of this variable.
A TensorShape
.
__init__
__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, 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 the variable to its initial value.
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.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.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 for details on how variables work in eager execution.
__abs__
__abs__( a, *args )
Computes the absolute value of a tensor.
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]
x
: A Tensor
or SparseTensor
of type float32
, float64
, int32
, int64
, complex64
or complex128
.name
: A name for the operation (optional).A Tensor
or SparseTensor
the same size and type as x
with absolute values. Note, for complex64
or complex128
input, the returned Tensor
will be of type float32
or float64
, respectively.
__add__
__add__( a, *args )
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, 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).A Tensor
. Has the same type as x
.
__and__
__and__( a, *args )
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__div__
__div__( a, *args )
Divide two values using Python 2 semantics. Used for Tensor.div.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
returns the quotient of x and y.
__floordiv__
__floordiv__( a, *args )
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.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
TypeError
: If the inputs are complex.__ge__
__ge__( a, *args )
Returns the truth value of (x >= y) element-wise.
NOTE: GreaterEqual
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
__getitem__
__getitem__( var, 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()
. 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.
var
: An ops.Variable
object.slice_spec
: The arguments to Tensor.__getitem__
.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.
ValueError
: If a slice range is negative size.TypeError
: If the slice indices aren't int, slice, or Ellipsis.__gt__
__gt__( a, *args )
Returns the truth value of (x > y) element-wise.
NOTE: Greater
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
__iadd__
__iadd__(other)
__idiv__
__idiv__(other)
__imul__
__imul__(other)
__invert__
__invert__( a, *args )
Returns the truth value of NOT x element-wise.
x
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__ipow__
__ipow__(other)
__irealdiv__
__irealdiv__(other)
__isub__
__isub__(other)
__iter__
__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.
TypeError
: when invoked.__itruediv__
__itruediv__(other)
__le__
__le__( a, *args )
Returns the truth value of (x <= y) element-wise.
NOTE: LessEqual
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
__lt__
__lt__( a, *args )
Returns the truth value of (x < y) element-wise.
NOTE: Less
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
__matmul__
__matmul__( a, *args )
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are: float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse
or b_is_sparse
flag to True
. These are False
by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a` # [[1, 2, 3], # [4, 5, 6]] a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) # 2-D tensor `b` # [[ 7, 8], # [ 9, 10], # [11, 12]] b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) # `a` * `b` # [[ 58, 64], # [139, 154]] c = tf.matmul(a, b) # 3-D tensor `a` # [[[ 1, 2, 3], # [ 4, 5, 6]], # [[ 7, 8, 9], # [10, 11, 12]]] a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3]) # 3-D tensor `b` # [[[13, 14], # [15, 16], # [17, 18]], # [[19, 20], # [21, 22], # [23, 24]]] b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2]) # `a` * `b` # [[[ 94, 100], # [229, 244]], # [[508, 532], # [697, 730]]] c = tf.matmul(a, b) # Since python >= 3.5 the @ operator is supported (see PEP 465). # In TensorFlow, it simply calls the `tf.matmul()` function, so the # following lines are equivalent: d = a @ b @ [[10.], [11.]] d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: Tensor
with same type and rank as a
.transpose_a
: If True
, a
is transposed before multiplication.transpose_b
: If True
, b
is transposed before multiplication.adjoint_a
: If True
, a
is conjugated and transposed before multiplication.adjoint_b
: If True
, b
is conjugated and transposed before multiplication.a_is_sparse
: If True
, a
is treated as a sparse matrix.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A Tensor
of the same type as a
and b
where each inner-most matrix is the product of the corresponding matrices in a
and b
, e.g. if all transpose or adjoint attributes are False
:
output
[..., i, j] = sum_k (a
[..., i, k] * b
[..., k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.ValueError
: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.__mod__
__mod__( a, *args )
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
NOTE: FloorMod
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: int32
, int64
, bfloat16
, half
, float32
, float64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
__mul__
__mul__( a, *args )
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__neg__
__neg__( a, *args )
Computes numerical negative value element-wise.
I.e., \(y = -x\).
x
: A Tensor
. Must be one of the following types: bfloat16
, half
, float32
, float64
, int32
, int64
, complex64
, complex128
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
__or__
__or__( a, *args )
Returns the truth value of x OR y element-wise.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__pow__
__pow__( a, *args )
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:
x = tf.constant([[2, 2], [3, 3]]) y = tf.constant([[8, 16], [2, 3]]) tf.pow(x, y) # [[256, 65536], [9, 27]]
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).A Tensor
.
__radd__
__radd__( a, *args )
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, 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).A Tensor
. Has the same type as x
.
__rand__
__rand__( a, *args )
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__rdiv__
__rdiv__( a, *args )
Divide two values using Python 2 semantics. Used for Tensor.div.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
returns the quotient of x and y.
__rfloordiv__
__rfloordiv__( a, *args )
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.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
TypeError
: If the inputs are complex.__rmatmul__
__rmatmul__( a, *args )
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are: float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse
or b_is_sparse
flag to True
. These are False
by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a` # [[1, 2, 3], # [4, 5, 6]] a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) # 2-D tensor `b` # [[ 7, 8], # [ 9, 10], # [11, 12]] b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) # `a` * `b` # [[ 58, 64], # [139, 154]] c = tf.matmul(a, b) # 3-D tensor `a` # [[[ 1, 2, 3], # [ 4, 5, 6]], # [[ 7, 8, 9], # [10, 11, 12]]] a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3]) # 3-D tensor `b` # [[[13, 14], # [15, 16], # [17, 18]], # [[19, 20], # [21, 22], # [23, 24]]] b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2]) # `a` * `b` # [[[ 94, 100], # [229, 244]], # [[508, 532], # [697, 730]]] c = tf.matmul(a, b) # Since python >= 3.5 the @ operator is supported (see PEP 465). # In TensorFlow, it simply calls the `tf.matmul()` function, so the # following lines are equivalent: d = a @ b @ [[10.], [11.]] d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: Tensor
with same type and rank as a
.transpose_a
: If True
, a
is transposed before multiplication.transpose_b
: If True
, b
is transposed before multiplication.adjoint_a
: If True
, a
is conjugated and transposed before multiplication.adjoint_b
: If True
, b
is conjugated and transposed before multiplication.a_is_sparse
: If True
, a
is treated as a sparse matrix.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A Tensor
of the same type as a
and b
where each inner-most matrix is the product of the corresponding matrices in a
and b
, e.g. if all transpose or adjoint attributes are False
:
output
[..., i, j] = sum_k (a
[..., i, k] * b
[..., k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.ValueError
: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.__rmod__
__rmod__( a, *args )
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
NOTE: FloorMod
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: int32
, int64
, bfloat16
, half
, float32
, float64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
__rmul__
__rmul__( a, *args )
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ror__
__ror__( a, *args )
Returns the truth value of x OR y element-wise.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__rpow__
__rpow__( a, *args )
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:
x = tf.constant([[2, 2], [3, 3]]) y = tf.constant([[8, 16], [2, 3]]) tf.pow(x, y) # [[256, 65536], [9, 27]]
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).A Tensor
.
__rsub__
__rsub__( a, *args )
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, 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).A Tensor
. Has the same type as x
.
__rtruediv__
__rtruediv__( a, *args )
__rxor__
__rxor__( a, *args )
x ^ y = (x | y) & ~(x & y).
__sub__
__sub__( a, *args )
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, 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).A Tensor
. Has the same type as x
.
__truediv__
__truediv__( a, *args )
__xor__
__xor__( a, *args )
x ^ y = (x | y) & ~(x & y).
assign
assign( value, use_locking=False )
Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value)
.
value
: A Tensor
. The new value for this variable.use_locking
: If True
, use locking during the assignment.A Tensor
that will hold the new value of this variable after the assignment has completed.
assign_add
assign_add( delta, use_locking=False )
Adds a value to this variable.
This is essentially a shortcut for assign_add(self, delta)
.
delta
: A Tensor
. The value to add to this variable.use_locking
: If True
, use locking during the operation.A Tensor
that will hold the new value of this variable after the addition has completed.
assign_sub
assign_sub( delta, use_locking=False )
Subtracts a value from this variable.
This is essentially a shortcut for assign_sub(self, delta)
.
delta
: A Tensor
. The value to subtract from this variable.use_locking
: If True
, use locking during the operation.A Tensor
that will hold the new value of this variable after the subtraction has completed.
count_up_to
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)
.
limit
: value at which incrementing the variable raises an error.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)
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.Session
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())
session
: The session to use to evaluate this variable. If none, the default session is used.A numpy ndarray
with a copy of the value of this variable.
from_proto
@staticmethod from_proto( variable_def, import_scope=None )
Returns a Variable
object created from variable_def
.
get_shape
get_shape()
Alias of Variable.shape.
initialized_value
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)
A Tensor
holding the value of this variable after its initializer has run.
load
load( value, session=None )
Load new value into this variable.
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.Session
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. 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]
value
: New variable valuesession
: The session to use to evaluate this variable. If none, the default session is used.ValueError
: Session is not passed and no default sessionread_value
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.
A Tensor
containing the value of the variable.
scatter_sub
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)
.
sparse_delta
: IndexedSlices
to be subtracted from this variable.use_locking
: If True
, use locking during the operation.A Tensor
that will hold the new value of this variable after the scattered subtraction has completed.
ValueError
: if sparse_delta
is not an IndexedSlices
.set_shape
set_shape(shape)
Overrides the shape for this variable.
shape
: the TensorShape
representing the overridden shape.to_proto
to_proto(export_scope=None)
Converts a Variable
to a VariableDef
protocol buffer.
export_scope
: Optional string
. Name scope to remove.A VariableDef
protocol buffer, or None
if the Variable
is not in the specified name scope.
value
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.
A Tensor
containing the value of the variable.
__array_priority__
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/Variable