tensorflow::error::Code tensorflow::Status::code() const

Member Details tensorflow::Status::Status() Create a success status. tensorflow::Status::~Status() tensorflow::Status::Status(tensorflow::error::Code code, tensorflow::StringPiece msg) Create a status with the specified error code and msg as a human-readable string containing more detailed information. tensorflow::Status::Status(const Status &s) Copy the specified status. void tensorflow::Status::operator=(const Status &s) bool tensorflow::Status::ok() const Returns true iff the status

string tensorflow::SessionOptions::target The TensorFlow runtime to connect to. If 'target' is empty or unspecified, the local TensorFlow runtime implementation will be used. Otherwise, the TensorFlow engine defined by 'target' will be used to perform all computations. "target" can be either a single entry or a comma separated list of entries. Each entry is a resolvable address of the following format: local ip:port host:port ... other system-specific formats to identify tasks and jobs ... NOT

tensorflow::SessionOptions::SessionOptions()

Env* tensorflow::SessionOptions::env The environment to use.

ConfigProto tensorflow::SessionOptions::config Configuration options.

Configuration information for a Session . Member Details Env* tensorflow::SessionOptions::env The environment to use. string tensorflow::SessionOptions::target The TensorFlow runtime to connect to. If 'target' is empty or unspecified, the local TensorFlow runtime implementation will be used. Otherwise, the TensorFlow engine defined by 'target' will be used to perform all computations. "target" can be either a single entry or a comma separated list of entries. Each entry is a resolvable address

virtual tensorflow::Session::~Session()

tensorflow::Session::Session()

virtual Status tensorflow::Session::Run(const std::vector< std::pair< string, Tensor > > &inputs, const std::vector< string > &output_tensor_names, const std::vector< string > &target_node_names, std::vector< Tensor > *outputs)=0 Runs the graph with the provided input tensors and fills outputs for the endpoints specified in output_tensor_names. Runs to but does not return Tensors for the nodes in target_node_names. The order of tensors in outputs will