org.tensorflow

Defines classes to build, save, load and execute TensorFlow models.

To get started, see the installation instructions.

The LabelImage example demonstrates use of this API to classify images using a pre-trained Inception architecture convolutional neural network. It demonstrates:

  • Graph construction: using the OperationBuilder class to construct a graph to decode, resize and normalize a JPEG image.
  • Model loading: Using Graph.importGraphDef() to load a pre-trained Inception model.
  • Graph execution: Using a Session to execute the graphs and find the best label for an image.

Additional examples can be found in the tensorflow/java GitHub repository.

Interfaces

Classes

EagerSession An environment for executing TensorFlow operations eagerly. 
EagerSession.Options  
Graph A data flow graph representing a TensorFlow computation. 
GraphOperation Implementation for an Operation added as a node to a Graph. 
GraphOperationBuilder An OperationBuilder for adding GraphOperations to a Graph. 
Output<T> A symbolic handle to a tensor produced by an Operation. 
SavedModelBundle SavedModelBundle represents a model loaded from storage. 
SavedModelBundle.Loader Options for loading a SavedModel. 
Server An in-process TensorFlow server, for use in distributed training. 
Session Driver for Graph execution. 
Session.Run Output tensors and metadata obtained when executing a session. 
Session.Runner Run Operations and evaluate Tensors. 
Shape The possibly partially known shape of a tensor produced by an operation. 
Tensor<T> A statically typed multi-dimensional array whose elements are of a type described by T. 
TensorFlow Static utility methods describing the TensorFlow runtime. 
Tensors Type-safe factory methods for creating Tensor objects. 

Enums

DataType Represents the type of elements in a Tensor as an enum. 
EagerSession.DevicePlacementPolicy Controls how to act when we try to run an operation on a given device but some input tensors are not on that device. 
EagerSession.ResourceCleanupStrategy Controls how TensorFlow resources are cleaned up when they are no longer needed. 

Exceptions