Broadcasting operation

An operation that uses numpy-style broadcasting to make the shapes of its tensor arguments compatible.


A piece of hardware that can run computation and has its own address space, like a GPU or CPU.


A method of Tensor that returns the value of the Tensor, triggering any graph computation required to determine the value. You may only call eval() on a Tensor in a graph that has been launched in a session.


TensorFlow's mechanism for patching a tensor directly into any node in a graph launched in a session. You apply feeds when you trigger the execution of a graph, not when you build the graph. A feed temporarily replaces a node with a tensor value. You supply feed data as an argument to a run() or eval() call that initiates computation. After the run the feed disappears and the original node definition remains. You usually designate specific nodes to be “feed” nodes by using tf.placeholder() to create them. See Basic Usage for more information.


TensorFlow's mechanism for retrieving tensors from a graph launched in a session. You retrieve fetches when you trigger the execution of a graph, not when you build the graph. To fetch the tensor value of a node or nodes, execute the graph with a run() call on the Session object and pass a list of names of nodes to retrieve. See Basic Usage for more information.


Describes a computation as a directed acyclic graph. Nodes in the graph represent operations that must be performed. Edges in the graph represent either data or control dependencies. GraphDef is the proto used to describe a graph to the system (it is the API), and consists of a collection of NodeDefs (see below). A GraphDef may be converted to a (C++) Graph object which is easier to operate on.


In the Python API, TensorFlow‘s representation of a tensor that is sparse along only its first dimension. If the tensor is k-dimensional, an IndexedSlices instance logically represents a collection of (k-1)-dimensional slices along the tensor’s first dimension. The indices of the slices are stored concatenated into a single 1-dimensional vector, and the corresponding slices are concatenated to form a single k-dimensional tensor. Use SparseTensor if the sparsity is not restricted to the first dimension.


An element of a graph.

Describes how to invoke a specific operation as one node in a specific computation Graph, including the values for any attrs needed to configure the operation. For operations that are polymorphic, the attrs include sufficient information to completely determine the signature of the Node. See graph.proto for details.

Op (operation)

In the TensorFlow runtime: A type of computation such as add or matmul or concat. You can add new ops to the runtime as described how to add an op.

In the Python API: A node in the graph. Ops are represented by instances of the class tf.Operation. The type property of an Operation indicates the run operation for the node, such as add or matmul.


The action of executing ops in a launched graph. Requires that the graph be launched in a Session.

In the Python API: A method of the Session class: You can pass tensors to feed and fetch to the run() call.

In the C++ API: A method of the tensorflow::Session.


A runtime object representing a launched graph. Provides methods to execute ops in the graph.

In the Python API: tf.Session

In the C++ API: class used to launch a graph and run operations tensorflow::Session.


The number of dimensions of a tensor and their sizes.

In a launched graph: Property of the tensors that flow between nodes. Some ops have strong requirements on the shape of their inputs and report errors at runtime if these are not met.

In the Python API: Attribute of a Python Tensor in the graph construction API. During constructions the shape of tensors can be only partially known, or even unknown. See tf.TensorShape

In the C++ API: class used to represent the shape of tensors tensorflow::TensorShape.


In the Python API, TensorFlow's representation of a tensor that is sparse in arbitrary positions. A SparseTensor stores only the non-empty values along with their indices, using a dictionary-of-keys format. In other words, if there are m non-empty values, it maintains a length-m vector of values and a matrix with m rows of indices. For efficiency, SparseTensor requires the indices to be sorted along increasing dimension number, i.e. in row-major order. Use IndexedSlices if the sparsity is only along the first dimension.


A Tensor is a typed multi-dimensional array. For example, a 4-D array of floating point numbers representing a mini-batch of images with dimensions [batch, height, width, channel].

In a launched graph: Type of the data that flow between nodes.

In the Python API: class used to represent the output and inputs of ops added to the graph tf.Tensor. Instances of this class do not hold data.

In the C++ API: class used to represent tensors returned from a Session::Run() call tensorflow::Tensor. Instances of this class hold data.