tensorflow - Rust (original) (raw)

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This crate provides Rust bindings for theTensorFlow machine learning library.

If you aren’t sure how to use something, please see theexamples folder.

• C API extensions to experiment with eager execution of kernels.
• This module builds computation graphs.
• A module for reading and writing TFRecords, Tensorflow’s preferred on-disk data format.
• This module exposes functions for building standard operations.
• This module supports building and training models.
• AttrMetadata describes the value of an attribute on an operation.
• This struct supports saving and restoring variables using Tensorflow checkpoints in SaveV2 format. First, the user creates a CheckpointMaker attached to an Scope indicating the list of variables to be saved/restored. The CheckpointMaker lazily modifies the graph creating the nodes needed for saving/restoring. When one wants to save/restore from or into a session, one calls the save/restore methods
• Metadata about a device.
• An opaque token for retrieving an output from a computation.
• Function is a grouping of operations with defined inputs and outputs. Once created and added to graphs, functions can be invoked by creating an operation whose operation type matches the function name.
• Options that can be passed during function creation.
• Represents a computation graph. Graphs may be shared between sessions. Graphs are thread-safe when used as directed.
• ImportGraphDefOptions holds options that can be passed toGraph::import_graph_def.
• ImportGraphDefResults holds results that are generated by Graph::import_graph_def_with_results().
• A Input is one end of a graph edge. It holds an operation and an index into the inputs of that operation.
• Dynamically loaded plugins. The C API doesn’t provide a way to unload libraries, so nothing happens when this goes out of scope.
• Contains data necessary to restart training, run inference. It can be used to serialize/de-serialize memory objects necessary for running computation in a graph when crossing the process boundary. It can be used for long term storage of graphs, cross-language execution of graphs, etc.
• An argument definition for a graph operation.
• An attribute definition for a graph operation.
• A Graph operation exposed from an external plugin
• Collection of OpDefs exposed from an external plugin
• An Operation is a node in a Graph. It is a computation which accepts inputs and produces outputs.
• An OperationDescription is an Operation in the process of being built (i.e. the builder pattern).
• Iterator over the operations in a Graph.
• A Output is one end of a graph edge. It holds an operation and an index into the outputs of that operation.
• Names a specific Output in the graph.
• PluggableDeviceLibrary handler.
• Quantized type for i8.
• Quantized type for i16.
• Quantized type for i32.
• Quantized type for u8.
• Quantized type for u16.
• Error generated while saving a model.
• Builds a SavedModelSaver, which can be used to save models.
• Aggregation type for a saved model bundle.
• Creates saved models. Use a SavedModelBuilder to create a SavedModelSaver.
• A Scope object represents a set of related TensorFlow ops that have the same properties such as a common name prefix.
• Manages a single graph and execution.
• Options that can be passed during session creation.
• Manages the inputs and outputs for a single execution of a graph.
• A Shape is the shape of a tensor. A Shape may be an unknown rank, or it may have a known rank with each dimension being known or unknown.
• SignatureDef defines the signature of a computation supported by a TensorFlow graph.
• Holds error information when communicating with back and forth with tensorflow.
• Holds a multi-dimensional array of elements of a single data type.
• Information about a Tensor necessary for feeding or retrieval.
• Holds state in the form of a tensor that persists across steps.
• Builds a Variable.
• A WhileBuilder is used to build a while loop.
• Describes the type of the value of an attribute on an operation.
• Error values that can be returned.
• Type of a single tensor element.
• Classification inputs.
• Classification method name used in a SignatureDef.
• Classification classes output.
• Classification scores output.
• Key in the signature def map for default serving signatures. The default signature is used in inference requests where a specific signature was not specified.
• Predict inputs.
• Prediction method name used in a SignatureDef.
• Predict outputs.
• Regression inputs.
• Regression method name used in a SignatureDef.
• Regression outputs.
• A Rust type that maps to a DataType.
• Returns a serialized KernelList protocol buffer containing KernelDefs for all registered kernels.
• Returns a serialized KernelList protocol buffer containing KernelDefs for all kernels registered for the operation named name.
• Returns a string describing version information of theTensorFlow library. TensorFlow is using semantic versioning.
• Deprecated alias for FetchToken.
• Convenience type for Result with Status as the error type.
• Deprecated alias for SessionRunArgs.