/TensorFlow 2.4


Represents a graph node that performs computation on tensors.

An Operation is a node in a tf.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) within a tf.function or under a tf.Graph.as_default context manager.

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

If a tf.compat.v1.Session is used, an Operation of a tf.Graph can be executed by passing it to tf.Session.run. op.run() is a shortcut for calling tf.compat.v1.get_default_session().run(op).

node_def node_def_pb2.NodeDef. NodeDef for the Operation. Used for attributes of node_def_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.
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.
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.

device The name of the device to which this op has been assigned, if any.
graph The Graph that contains this operation.
inputs The sequence of Tensor objects representing the data inputs of this op.
name The full name of this operation.
node_def Returns the NodeDef representation of this operation.
op_def Returns the OpDef proto that represents the type of this op.
outputs The list of Tensor objects representing the outputs of this op.
traceback Returns the call stack from when this operation was constructed.
type The type of the op (e.g. "MatMul").



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Returns the list of colocation groups of the op.


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

name The name of the attr to fetch.
The value of the attr, as a Python object.
ValueError If this op does not have an attr with the given name.


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Runs this operation in a Session.

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

Note: 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.
feed_dict A dictionary that maps Tensor objects to feed values. See tf.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.


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DEPRECATED: Use outputs.

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.