Core graph data structures

class tf.Graph

A TensorFlow computation, represented as a dataflow graph.

A Graph contains a set of Operation objects, which represent units of computation; and Tensor objects, which represent the units of data that flow between operations.

A default Graph is always registered, and accessible by calling tf.get_default_graph(). To add an operation to the default graph, simply call one of the functions that defines a new Operation:

c = tf.constant(4.0)
assert c.graph is tf.get_default_graph()

Another typical usage involves the Graph.as_default() context manager, which overrides the current default graph for the lifetime of the context:

g = tf.Graph()
with g.as_default():
  # Define operations and tensors in `g`.
  c = tf.constant(30.0)
  assert c.graph is g

Important note: This class is not thread-safe for graph construction. All operations should be created from a single thread, or external synchronization must be provided. Unless otherwise specified, all methods are not thread-safe.


tf.Graph.__init__() {:#Graph.init}

Creates a new, empty Graph.


tf.Graph.as_default()

Returns a context manager that makes this Graph the default graph.

This method should be used if you want to create multiple graphs in the same process. For convenience, a global default graph is provided, and all ops will be added to this graph if you do not create a new graph explicitly. Use this method with the with keyword to specify that ops created within the scope of a block should be added to this graph.

The default graph is a property of the current thread. If you create a new thread, and wish to use the default graph in that thread, you must explicitly add a with g.as_default(): in that thread's function.

The following code examples are equivalent:

# 1. Using Graph.as_default():
g = tf.Graph()
with g.as_default():
  c = tf.constant(5.0)
  assert c.graph is g

# 2. Constructing and making default:
with tf.Graph().as_default() as g:
  c = tf.constant(5.0)
  assert c.graph is g
Returns:

A context manager for using this graph as the default graph.


tf.Graph.as_graph_def(from_version=None, add_shapes=False)

Returns a serialized GraphDef representation of this graph.

The serialized GraphDef can be imported into another Graph (using import_graph_def()) or used with the C++ Session API.

This method is thread-safe.

Args:
  • from_version: Optional. If this is set, returns a GraphDef containing only the nodes that were added to this graph since its version property had the given value.
  • add_shapes: If true, adds an "_output_shapes" list attr to each node with the inferred shapes of each of its outputs.
Returns:

A GraphDef protocol buffer.

Raises:
  • ValueError: If the graph_def would be too large.

tf.Graph.finalize()

Finalizes this graph, making it read-only.

After calling g.finalize(), no new operations can be added to g. This method is used to ensure that no operations are added to a graph when it is shared between multiple threads, for example when using a QueueRunner.


tf.Graph.finalized

True if this graph has been finalized.


tf.Graph.control_dependencies(control_inputs)

Returns a context manager that specifies control dependencies.

Use with the with keyword to specify that all operations constructed within the context should have control dependencies on control_inputs. For example:

with g.control_dependencies([a, b, c]):
  # `d` and `e` will only run after `a`, `b`, and `c` have executed.
  d = ...
  e = ...

Multiple calls to control_dependencies() can be nested, and in that case a new Operation will have control dependencies on the union of control_inputs from all active contexts.

with g.control_dependencies([a, b]):
  # Ops constructed here run after `a` and `b`.
  with g.control_dependencies([c, d]):
    # Ops constructed here run after `a`, `b`, `c`, and `d`.

You can pass None to clear the control dependencies:

with g.control_dependencies([a, b]):
  # Ops constructed here run after `a` and `b`.
  with g.control_dependencies(None):
    # Ops constructed here run normally, not waiting for either `a` or `b`.
    with g.control_dependencies([c, d]):
      # Ops constructed here run after `c` and `d`, also not waiting
      # for either `a` or `b`.

N.B. The control dependencies context applies only to ops that are constructed within the context. Merely using an op or tensor in the context does not add a control dependency. The following example illustrates this point:

# WRONG
def my_func(pred, tensor):
  t = tf.matmul(tensor, tensor)
  with tf.control_dependencies([pred]):
    # The matmul op is created outside the context, so no control
    # dependency will be added.
    return t

# RIGHT
def my_func(pred, tensor):
  with tf.control_dependencies([pred]):
    # The matmul op is created in the context, so a control dependency
    # will be added.
    return tf.matmul(tensor, tensor)
Args:
  • control_inputs: A list of Operation or Tensor objects which must be executed or computed before running the operations defined in the context. Can also be None to clear the control dependencies.
Returns:

A context manager that specifies control dependencies for all operations constructed within the context.

Raises:
  • TypeError: If control_inputs is not a list of Operation or Tensor objects.

tf.Graph.device(device_name_or_function)

Returns a context manager that specifies the default device to use.

The device_name_or_function argument may either be a device name string, a device function, or None:

  • If it is a device name string, all operations constructed in this context will be assigned to the device with that name, unless overridden by a nested device() context.
  • If it is a function, it will be treated as a function from Operation objects to device name strings, and invoked each time a new Operation is created. The Operation will be assigned to the device with the returned name.
  • If it is None, all device() invocations from the enclosing context will be ignored.

For information about the valid syntax of device name strings, see the documentation in DeviceNameUtils.

For example:

with g.device('/gpu:0'):
  # All operations constructed in this context will be placed
  # on GPU 0.
  with g.device(None):
    # All operations constructed in this context will have no
    # assigned device.

# Defines a function from `Operation` to device string.
def matmul_on_gpu(n):
  if n.type == "MatMul":
    return "/gpu:0"
  else:
    return "/cpu:0"

with g.device(matmul_on_gpu):
  # All operations of type "MatMul" constructed in this context
  # will be placed on GPU 0; all other operations will be placed
  # on CPU 0.

N.B. The device scope may be overridden by op wrappers or other library code. For example, a variable assignment op v.assign() must be colocated with the tf.Variable v, and incompatible device scopes will be ignored.

Args:
  • device_name_or_function: The device name or function to use in the context.
Returns:

A context manager that specifies the default device to use for newly created ops.


tf.Graph.name_scope(name)

Returns a context manager that creates hierarchical names for operations.

A graph maintains a stack of name scopes. A with name_scope(...): statement pushes a new name onto the stack for the lifetime of the context.

The name argument will be interpreted as follows:

  • A string (not ending with '/') will create a new name scope, in which name is appended to the prefix of all operations created in the context. If name has been used before, it will be made unique by calling self.unique_name(name).
  • A scope previously captured from a with g.name_scope(...) as scope: statement will be treated as an "absolute" name scope, which makes it possible to re-enter existing scopes.
  • A value of None or the empty string will reset the current name scope to the top-level (empty) name scope.

For example:

with tf.Graph().as_default() as g:
  c = tf.constant(5.0, name="c")
  assert c.op.name == "c"
  c_1 = tf.constant(6.0, name="c")
  assert c_1.op.name == "c_1"

  # Creates a scope called "nested"
  with g.name_scope("nested") as scope:
    nested_c = tf.constant(10.0, name="c")
    assert nested_c.op.name == "nested/c"

    # Creates a nested scope called "inner".
    with g.name_scope("inner"):
      nested_inner_c = tf.constant(20.0, name="c")
      assert nested_inner_c.op.name == "nested/inner/c"

    # Create a nested scope called "inner_1".
    with g.name_scope("inner"):
      nested_inner_1_c = tf.constant(30.0, name="c")
      assert nested_inner_1_c.op.name == "nested/inner_1/c"

      # Treats `scope` as an absolute name scope, and
      # switches to the "nested/" scope.
      with g.name_scope(scope):
        nested_d = tf.constant(40.0, name="d")
        assert nested_d.op.name == "nested/d"

        with g.name_scope(""):
          e = tf.constant(50.0, name="e")
          assert e.op.name == "e"

The name of the scope itself can be captured by with g.name_scope(...) as scope:, which stores the name of the scope in the variable scope. This value can be used to name an operation that represents the overall result of executing the ops in a scope. For example:

inputs = tf.constant(...)
with g.name_scope('my_layer') as scope:
  weights = tf.Variable(..., name="weights")
  biases = tf.Variable(..., name="biases")
  affine = tf.matmul(inputs, weights) + biases
  output = tf.nn.relu(affine, name=scope)

NOTE: This constructor validates the given name. Valid scope names match one of the following regular expressions:

[A-Za-z0-9.][A-Za-z0-9_.\\-/]* (for scopes at the root)
[A-Za-z0-9_.\\-/]* (for other scopes)
Args:
  • name: A name for the scope.
Returns:

A context manager that installs name as a new name scope.

Raises:
  • ValueError: If name is not a valid scope name. The rules are the

A Graph instance supports an arbitrary number of "collections" that are identified by name. For convenience when building a large graph, collections can store groups of related objects: for example, the tf.Variable uses a collection (named tf.GraphKeys.VARIABLES) for all variables that are created during the construction of a graph. The caller may define additional collections by specifying a new name.


tf.Graph.add_to_collection(name, value)

Stores value in the collection with the given name.

Note that collections are not sets, so it is possible to add a value to a collection several times.

Args:
  • name: The key for the collection. The GraphKeys class contains many standard names for collections.
  • value: The value to add to the collection.

tf.Graph.add_to_collections(names, value)

Stores value in the collections given by names.

Note that collections are not sets, so it is possible to add a value to a collection several times. This function makes sure that duplicates in names are ignored, but it will not check for pre-existing membership of value in any of the collections in names.

names can be any iterable, but if names is a string, it is treated as a single collection name.

Args:
  • names: The keys for the collections to add to. The GraphKeys class contains many standard names for collections.
  • value: The value to add to the collections.

tf.Graph.get_collection(name, scope=None)

Returns a list of values in the collection with the given name.

This is different from get_collection_ref() which always returns the actual collection list if it exists in that it returns a new list each time it is called.

Args:
  • name: The key for the collection. For example, the GraphKeys class contains many standard names for collections.
  • scope: (Optional.) If supplied, the resulting list is filtered to include only items whose name attribute matches using re.match. Items without a name attribute are never returned if a scope is supplied and the choice or re.match means that a scope without special tokens filters by prefix.
Returns:

The list of values in the collection with the given name, or an empty list if no value has been added to that collection. The list contains the values in the order under which they were collected.


tf.Graph.get_collection_ref(name)

Returns a list of values in the collection with the given name.

If the collection exists, this returns the list itself, which can be modified in place to change the collection. If the collection does not exist, it is created as an empty list and the list is returned.

This is different from get_collection() which always returns a copy of the collection list if it exists and never creates an empty collection.

Args:
  • name: The key for the collection. For example, the GraphKeys class contains many standard names for collections.
Returns:

The list of values in the collection with the given name, or an empty list if no value has been added to that collection.


tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)

Returns the object referred to by obj, as an Operation or Tensor.

This function validates that obj represents an element of this graph, and gives an informative error message if it is not.

This function is the canonical way to get/validate an object of one of the allowed types from an external argument reference in the Session API.

This method may be called concurrently from multiple threads.

Args:
  • obj: A Tensor, an Operation, or the name of a tensor or operation. Can also be any object with an _as_graph_element() method that returns a value of one of these types.
  • allow_tensor: If true, obj may refer to a Tensor.
  • allow_operation: If true, obj may refer to an Operation.
Returns:

The Tensor or Operation in the Graph corresponding to obj.

Raises:
  • TypeError: If obj is not a type we support attempting to convert to types.
  • ValueError: If obj is of an appropriate type but invalid. For example, an invalid string.
  • KeyError: If obj is not an object in the graph.

tf.Graph.get_operation_by_name(name)

Returns the Operation with the given name.

This method may be called concurrently from multiple threads.

Args:
  • name: The name of the Operation to return.
Returns:

The Operation with the given name.

Raises:
  • TypeError: If name is not a string.
  • KeyError: If name does not correspond to an operation in this graph.

tf.Graph.get_tensor_by_name(name)

Returns the Tensor with the given name.

This method may be called concurrently from multiple threads.

Args:
  • name: The name of the Tensor to return.
Returns:

The Tensor with the given name.

Raises:
  • TypeError: If name is not a string.
  • KeyError: If name does not correspond to a tensor in this graph.

tf.Graph.get_operations()

Return the list of operations in the graph.

You can modify the operations in place, but modifications to the list such as inserts/delete have no effect on the list of operations known to the graph.

This method may be called concurrently from multiple threads.

Returns:

A list of Operations.


tf.Graph.seed

The graph-level random seed of this graph.


tf.Graph.unique_name(name, mark_as_used=True)

Return a unique operation name for name.

unique_name is used to generate structured names, separated by "/", to help identify operations when debugging a graph. Operation names are displayed in error messages reported by the TensorFlow runtime, and in various visualization tools such as TensorBoard.

If mark_as_used is set to True, which is the default, a new unique name is created and marked as in use. If it's set to False, the unique name is returned without actually being marked as used. This is useful when the caller simply wants to know what the name to be created will be.

Args:
  • name: The name for an operation.
  • mark_as_used: Whether to mark this name as being used.
Returns:

A string to be passed to create_op() that will be used to name the operation being created.


tf.Graph.version

Returns a version number that increases as ops are added to the graph.

Note that this is unrelated to the GraphDef version.


tf.Graph.graph_def_versions

The GraphDef version information of this graph.

For details on the meaning of each version, see [GraphDef] (https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).

Returns:

A VersionDef.


tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True)

Creates an Operation in this graph.

This is a low-level interface for creating an Operation. Most programs will not call this method directly, and instead use the Python op constructors, such as tf.constant(), which add ops to the default graph.

Args:
  • op_type: The Operation type to create. This corresponds to the OpDef.name field for the proto that defines the operation.
  • inputs: A list of Tensor objects that will be inputs to the Operation.
  • dtypes: A list of DType objects that will be the types of the tensors that the operation produces.
  • input_types: (Optional.) A list of DTypes that will be the types of the tensors that the operation consumes. By default, uses the base DType of each input in inputs. Operations that expect reference-typed inputs must specify input_types explicitly.
  • name: (Optional.) A string name for the operation. If not specified, a name is generated based on op_type.
  • attrs: (Optional.) A dictionary where the key is the attribute name (a string) and the value is the respective attr attribute of the NodeDef proto that will represent the operation (an AttrValue proto).
  • op_def: (Optional.) The OpDef proto that describes the op_type that the operation will have.
  • compute_shapes: (Optional.) If True, shape inference will be performed to compute the shapes of the outputs.
  • compute_device: (Optional.) If True, device functions will be executed to compute the device property of the Operation.
Raises:
  • TypeError: if any of the inputs is not a Tensor.
  • ValueError: if colocation conflicts with existing device assignment.
Returns:

An Operation object.


tf.Graph.gradient_override_map(op_type_map)

EXPERIMENTAL: A context manager for overriding gradient functions.

This context manager can be used to override the gradient function that will be used for ops within the scope of the context.

For example:

@tf.RegisterGradient("CustomSquare")
def _custom_square_grad(op, grad):
  # ...

with tf.Graph().as_default() as g:
  c = tf.constant(5.0)
  s_1 = tf.square(c)  # Uses the default gradient for tf.square.
  with g.gradient_override_map({"Square": "CustomSquare"}):
    s_2 = tf.square(s_2)  # Uses _custom_square_grad to compute the
                          # gradient of s_2.
Args:
  • op_type_map: A dictionary mapping op type strings to alternative op type strings.
Returns:

A context manager that sets the alternative op type to be used for one or more ops created in that context.

Raises:
  • TypeError: If op_type_map is not a dictionary mapping strings to strings.

Other Methods


tf.Graph.colocate_with(op, ignore_existing=False)

Returns a context manager that specifies an op to colocate with.

For example:

a = tf.Variable([1.0])
with g.colocate_with(a):
  b = tf.constant(1.0)
  c = tf.add(a, b)

b and c will always be colocated with a, no matter where a is eventually placed.

Args:
  • op: The op to colocate all created ops with.
  • ignore_existing: If true, only applies colocation of this op within the context, rather than applying all colocation properties on the stack.
Raises:
  • ValueError: if op is None.
Yields:

A context manager that specifies the op with which to colocate newly created ops.


tf.Graph.container(container_name)

Returns a context manager that specifies the resource container to use.

Stateful operations, such as variables and queues, can maintain their states on devices so that they can be shared by multiple processes. A resource container is a string name under which these stateful operations are tracked. These resources can be released or cleared with tf.Session.reset().

For example:

with g.container('experiment0'):
  # All stateful Operations constructed in this context will be placed
  # in resource container "experiment0".
  v1 = tf.Variable([1.0])
  v2 = tf.Variable([2.0])
  with g.container("experiment1"):
    # All stateful Operations constructed in this context will be
    # placed in resource container "experiment1".
    v3 = tf.Variable([3.0])
    q1 = tf.FIFOQueue(10, tf.float32)
  # All stateful Operations constructed in this context will be
  # be created in the "experiment0".
  v4 = tf.Variable([4.0])
  q1 = tf.FIFOQueue(20, tf.float32)
  with g.container(""):
    # All stateful Operations constructed in this context will be
    # be placed in the default resource container.
    v5 = tf.Variable([5.0])
    q3 = tf.FIFOQueue(30, tf.float32)

# Resets container "experiment0", after which the state of v1, v2, v4, q1
# will become undefined (such as unitialized).
tf.Session.reset(target, ["experiment0"])
Args:
  • container_name: container name string.
Returns:

A context manager for defining resource containers for stateful ops, yields the container name.


tf.Graph.get_all_collection_keys()

Returns a list of collections used in this graph.


tf.Graph.is_feedable(tensor)

Returns True if and only if tensor is feedable.


tf.Graph.is_fetchable(tensor_or_op)

Returns True if and only if tensor_or_op is fetchable.


tf.Graph.prevent_feeding(tensor)

Marks the given tensor as unfeedable in this graph.


tf.Graph.prevent_fetching(op)

Marks the given op as unfetchable in this graph.


class tf.Operation

Represents a graph node that performs computation on tensors.

An Operation is a node in a TensorFlow Graph that takes zero or more Tensor objects as input, and produces zero or more Tensor objects as output. Objects of type Operation are created by calling a Python op constructor (such as tf.matmul()) or Graph.create_op().

For example c = tf.matmul(a, b) creates an Operation of type "MatMul" that takes tensors a and b as input, and produces c as output.

After the graph has been launched in a session, an Operation can be executed by passing it to Session.run(). op.run() is a shortcut for calling tf.get_default_session().run(op).


tf.Operation.name

The full name of this operation.


tf.Operation.type

The type of the op (e.g. "MatMul").


tf.Operation.inputs

The list of Tensor objects representing the data inputs of this op.


tf.Operation.control_inputs

The Operation objects on which this op has a control dependency.

Before this op is executed, TensorFlow will ensure that the operations in self.control_inputs have finished executing. This mechanism can be used to run ops sequentially for performance reasons, or to ensure that the side effects of an op are observed in the correct order.

Returns:

A list of Operation objects.


tf.Operation.outputs

The list of Tensor objects representing the outputs of this op.


tf.Operation.device

The name of the device to which this op has been assigned, if any.

Returns:

The string name of the device to which this op has been assigned, or an empty string if it has not been assigned to a device.


tf.Operation.graph

The Graph that contains this operation.


tf.Operation.run(feed_dict=None, session=None)

Runs this operation in a Session.

Calling this method will execute all preceding operations that produce the inputs needed for this operation.

N.B. Before invoking Operation.run(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.

Args:
  • feed_dict: A dictionary that maps Tensor objects to feed values. See Session.run() for a description of the valid feed values.
  • session: (Optional.) The Session to be used to run to this operation. If none, the default session will be used.

tf.Operation.get_attr(name)

Returns the value of the attr of this op with the given name.

Args:
  • name: The name of the attr to fetch.
Returns:

The value of the attr, as a Python object.

Raises:
  • ValueError: If this op does not have an attr with the given name.

tf.Operation.traceback

Returns the call stack from when this operation was constructed.

Other Methods


tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None) {:#Operation.init}

Creates an Operation.

NOTE: This constructor validates the name of the Operation (passed as node_def.name). Valid Operation names match the following regular expression:

[A-Za-z0-9.][A-Za-z0-9_.\-/]*
Args:
  • node_def: graph_pb2.NodeDef. NodeDef for the Operation. Used for attributes of graph_pb2.NodeDef, typically name, op, and device. The input attribute is irrelevant here as it will be computed when generating the model.
  • g: Graph. The parent graph.
  • inputs: list of Tensor objects. The inputs to this Operation.
  • output_types: list of DType objects. List of the types of the Tensors computed by this operation. The length of this list indicates the number of output endpoints of the Operation.
  • control_inputs: list of operations or tensors from which to have a control dependency.
  • input_types: List of DType objects representing the types of the tensors accepted by the Operation. By default uses [x.dtype.base_dtype for x in inputs]. Operations that expect reference-typed inputs must specify these explicitly.
  • original_op: Optional. Used to associate the new Operation with an existing Operation (for example, a replica with the op that was replicated).
  • op_def: Optional. The op_def_pb2.OpDef proto that describes the op type that this Operation represents.
Raises:
  • TypeError: if control inputs are not Operations or Tensors, or if node_def is not a NodeDef, or if g is not a Graph, or if inputs are not tensors, or if inputs and input_types are incompatible.
  • ValueError: if the node_def name is not valid.

tf.Operation.colocation_groups()

Returns the list of colocation groups of the op.


tf.Operation.node_def

Returns a serialized NodeDef representation of this operation.

Returns:

A NodeDef protocol buffer.


tf.Operation.op_def

Returns the OpDef proto that represents the type of this op.

Returns:

An OpDef protocol buffer.


tf.Operation.values()

DEPRECATED: Use outputs.


class tf.Tensor

Represents one of the outputs of an Operation.

Note: the Tensor class will be replaced by Output in the future. Currently these two are aliases for each other.

A Tensor is a symbolic handle to one of the outputs of an Operation. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow Session.

This class has two primary purposes:

  1. A Tensor can be passed as an input to another Operation. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire Graph that represents a large, multi-step computation.

  2. After the graph has been launched in a session, the value of the Tensor can be computed by passing it to Session.run(). t.eval() is a shortcut for calling tf.get_default_session().run(t).

In the following example, c, d, and e are symbolic Tensor objects, whereas result is a numpy array that stores a concrete value:

# Build a dataflow graph.
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)

# Construct a `Session` to execute the graph.
sess = tf.Session()

# Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e)

tf.Tensor.dtype

The DType of elements in this tensor.


tf.Tensor.name

The string name of this tensor.


tf.Tensor.value_index

The index of this tensor in the outputs of its Operation.


tf.Tensor.graph

The Graph that contains this tensor.


tf.Tensor.op

The Operation that produces this tensor as an output.


tf.Tensor.consumers()

Returns a list of Operations that consume this tensor.

Returns:

A list of Operations.


tf.Tensor.eval(feed_dict=None, session=None)

Evaluates this tensor in a Session.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

N.B. Before invoking Tensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly.

Args:
  • feed_dict: A dictionary that maps Tensor objects to feed values. See Session.run() for a description of the valid feed values.
  • session: (Optional.) The Session to be used to evaluate this tensor. If none, the default session will be used.
Returns:

A numpy array corresponding to the value of this tensor.


tf.Tensor.get_shape()

Returns the TensorShape that represents the shape of this tensor.

The shape is computed using shape inference functions that are registered for each Operation type using tf.RegisterShape. See TensorShape for more details of what a shape represents.

The inferred shape of a tensor is used to provide shape information without having to launch the graph in a session. This can be used for debugging, and providing early error messages. For example:

c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])

print(c.get_shape())
==> TensorShape([Dimension(2), Dimension(3)])

d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])

print(d.get_shape())
==> TensorShape([Dimension(4), Dimension(2)])

# Raises a ValueError, because `c` and `d` do not have compatible
# inner dimensions.
e = tf.matmul(c, d)

f = tf.matmul(c, d, transpose_a=True, transpose_b=True)

print(f.get_shape())
==> TensorShape([Dimension(3), Dimension(4)])

In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, Tensor.set_shape() can be used to augment the inferred shape.

Returns:

A TensorShape representing the shape of this tensor.


tf.Tensor.set_shape(shape)

Updates the shape of this tensor.

This method can be called multiple times, and will merge the given shape with the current shape of this tensor. It can be used to provide additional information about the shape of this tensor that cannot be inferred from the graph alone. For example, this can be used to provide additional information about the shapes of images:

_, image_data = tf.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)

# The height and width dimensions of `image` are data dependent, and
# cannot be computed without executing the op.
print(image.get_shape())
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])

# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.get_shape())
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
Args:
  • shape: A TensorShape representing the shape of this tensor.
Raises:
  • ValueError: If shape is not compatible with the current shape of this tensor.

Other Methods


tf.Tensor.__init__(op, value_index, dtype) {:#Tensor.init}

Creates a new Tensor.

Args:
  • op: An Operation. Operation that computes this tensor.
  • value_index: An int. Index of the operation's endpoint that produces this tensor.
  • dtype: A DType. Type of elements stored in this tensor.
Raises:
  • TypeError: If the op is not an Operation.

tf.Tensor.device

The name of the device on which this tensor will be produced, or None.