Uses of Package
ai.onnx.proto
Packages that use ai.onnx.proto
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides an interface to LibLinear-java for classification problems.
Provides an interface to LibSVM for classification problems.
Provides the base classes for models trained with stochastic gradient descent.
Provides an interface for model prediction combinations,
two base classes for ensemble models, a base class for
ensemble excuses, and a Bagging implementation.
Provides an interface to liblinear for regression problems.
Provides an interface to LibSVM for regression problems.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Interfaces and utilities for writing ONNX models from Java.
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Classes in ai.onnx.proto used by ai.onnx.protoClassDescriptionAttributes A named attribute containing either singular float, integer, string, graph, and tensor values, or repeated float, integer, string, graph, and tensor values.Note: this enum is structurally identical to the OpSchema::AttrType enum defined in schema.h.Attributes A named attribute containing either singular float, integer, string, graph, and tensor values, or repeated float, integer, string, graph, and tensor values.Graphs A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs.Graphs A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs.Models ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.Models ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.Nodes Computation graphs are made up of a DAG of nodes, which represent what is commonly called a "layer" or "pipeline stage" in machine learning frameworks.Nodes Computation graphs are made up of a DAG of nodes, which represent what is commonly called a "layer" or "pipeline stage" in machine learning frameworks.Operator Sets OperatorSets are uniquely identified by a (domain, opset_version) pair.Operator Sets OperatorSets are uniquely identified by a (domain, opset_version) pair.A serialized sparse-tensor valueA serialized sparse-tensor valueStringStringEntryProto follows the pattern for cross-proto-version maps.StringStringEntryProto follows the pattern for cross-proto-version maps.Protobuf type
onnx.TensorAnnotationProtobuf typeonnx.TensorAnnotationTensors A serialized tensor value.Tensors A serialized tensor value.Location of the data for this tensor.Protobuf enumonnx.TensorProto.DataTypeFor very large tensors, we may want to store them in chunks, in which case the following fields will specify the segment that is stored in the current TensorProto.For very large tensors, we may want to store them in chunks, in which case the following fields will specify the segment that is stored in the current TensorProto.Defines a tensor shape.Defines a tensor shape.Protobuf typeonnx.TensorShapeProto.DimensionProtobuf typeonnx.TensorShapeProto.DimensionTraining information TrainingInfoProto stores information for training a model.Training information TrainingInfoProto stores information for training a model.Types The standard ONNX data types.Types The standard ONNX data types.map<K,V>map<K,V>Protobuf typeonnx.TypeProto.OpaqueProtobuf typeonnx.TypeProto.Opaquerepeated Trepeated TProtobuf typeonnx.TypeProto.SparseTensorProtobuf typeonnx.TypeProto.SparseTensorProtobuf typeonnx.TypeProto.TensorProtobuf typeonnx.TypeProto.TensorDefines information on value, including the name, the type, and the shape of the value.Defines information on value, including the name, the type, and the shape of the value.Versioning ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md To be compatible with both proto2 and proto3, we will use a version number that is not defined by the default value but an explicit enum number. -
Classes in ai.onnx.proto used by org.tribuoClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.classification.liblinearClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.classification.libsvmClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.common.sgdClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.ensembleClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.regression.liblinearClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.regression.libsvmClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.regression.slmClassDescriptionModels ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.
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Classes in ai.onnx.proto used by org.tribuo.util.onnxClassDescriptionAttributes A named attribute containing either singular float, integer, string, graph, and tensor values, or repeated float, integer, string, graph, and tensor values.Note: this enum is structurally identical to the OpSchema::AttrType enum defined in schema.h.Graphs A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs.Nodes Computation graphs are made up of a DAG of nodes, which represent what is commonly called a "layer" or "pipeline stage" in machine learning frameworks.Operator Sets OperatorSets are uniquely identified by a (domain, opset_version) pair.Tensors A serialized tensor value.Protobuf enum
onnx.TensorProto.DataTypeTypes The standard ONNX data types.