Uses of Class
ai.onnx.proto.OnnxMl.StringStringEntryProto
Packages that use OnnxMl.StringStringEntryProto
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Uses of OnnxMl.StringStringEntryProto in ai.onnx.proto
Fields in ai.onnx.proto with type parameters of type OnnxMl.StringStringEntryProtoModifier and TypeFieldDescriptionstatic final com.google.protobuf.Parser<OnnxMl.StringStringEntryProto>OnnxMl.StringStringEntryProto.PARSERDeprecated.Methods in ai.onnx.proto that return OnnxMl.StringStringEntryProtoModifier and TypeMethodDescriptionOnnxMl.StringStringEntryProto.Builder.build()OnnxMl.StringStringEntryProto.Builder.buildPartial()OnnxMl.StringStringEntryProto.getDefaultInstance()OnnxMl.StringStringEntryProto.Builder.getDefaultInstanceForType()OnnxMl.StringStringEntryProto.getDefaultInstanceForType()OnnxMl.TensorProto.Builder.getExternalData(int index) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProto.getExternalData(int index) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProtoOrBuilder.getExternalData(int index) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.getInitializationBinding(int index) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.TrainingInfoProto.getInitializationBinding(int index) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.TrainingInfoProtoOrBuilder.getInitializationBinding(int index) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.ModelProto.Builder.getMetadataProps(int index) Named metadata values; keys should be distinct.OnnxMl.ModelProto.getMetadataProps(int index) Named metadata values; keys should be distinct.OnnxMl.ModelProtoOrBuilder.getMetadataProps(int index) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNames(int index) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotation.getQuantParameterTensorNames(int index) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotationOrBuilder.getQuantParameterTensorNames(int index) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.getUpdateBinding(int index) Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProto.getUpdateBinding(int index) Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProtoOrBuilder.getUpdateBinding(int index) Gradient-based training is usually an iterative procedure.OnnxMl.StringStringEntryProto.parseDelimitedFrom(InputStream input) OnnxMl.StringStringEntryProto.parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.parseFrom(byte[] data) OnnxMl.StringStringEntryProto.parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.parseFrom(com.google.protobuf.ByteString data) OnnxMl.StringStringEntryProto.parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.parseFrom(com.google.protobuf.CodedInputStream input) OnnxMl.StringStringEntryProto.parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.parseFrom(InputStream input) OnnxMl.StringStringEntryProto.parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.parseFrom(ByteBuffer data) OnnxMl.StringStringEntryProto.parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) Methods in ai.onnx.proto that return types with arguments of type OnnxMl.StringStringEntryProtoModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.getExternalDataList()Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProto.getExternalDataList()Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProtoOrBuilder.getExternalDataList()Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.getInitializationBindingList()This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.TrainingInfoProto.getInitializationBindingList()This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.TrainingInfoProtoOrBuilder.getInitializationBindingList()This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.ModelProto.Builder.getMetadataPropsList()Named metadata values; keys should be distinct.OnnxMl.ModelProto.getMetadataPropsList()Named metadata values; keys should be distinct.OnnxMl.ModelProtoOrBuilder.getMetadataPropsList()Named metadata values; keys should be distinct.com.google.protobuf.Parser<OnnxMl.StringStringEntryProto>OnnxMl.StringStringEntryProto.getParserForType()OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNamesList()<key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotation.getQuantParameterTensorNamesList()<key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotationOrBuilder.getQuantParameterTensorNamesList()<key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.getUpdateBindingList()Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProto.getUpdateBindingList()Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProtoOrBuilder.getUpdateBindingList()Gradient-based training is usually an iterative procedure.static com.google.protobuf.Parser<OnnxMl.StringStringEntryProto>OnnxMl.StringStringEntryProto.parser()Methods in ai.onnx.proto with parameters of type OnnxMl.StringStringEntryProtoModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.addExternalData(int index, OnnxMl.StringStringEntryProto value) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProto.Builder.addExternalData(OnnxMl.StringStringEntryProto value) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.addInitializationBinding(int index, OnnxMl.StringStringEntryProto value) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.TrainingInfoProto.Builder.addInitializationBinding(OnnxMl.StringStringEntryProto value) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.ModelProto.Builder.addMetadataProps(int index, OnnxMl.StringStringEntryProto value) Named metadata values; keys should be distinct.OnnxMl.ModelProto.Builder.addMetadataProps(OnnxMl.StringStringEntryProto value) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto value) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(OnnxMl.StringStringEntryProto value) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(int index, OnnxMl.StringStringEntryProto value) Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(OnnxMl.StringStringEntryProto value) Gradient-based training is usually an iterative procedure.OnnxMl.StringStringEntryProto.Builder.mergeFrom(OnnxMl.StringStringEntryProto other) OnnxMl.StringStringEntryProto.newBuilder(OnnxMl.StringStringEntryProto prototype) OnnxMl.TensorProto.Builder.setExternalData(int index, OnnxMl.StringStringEntryProto value) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.setInitializationBinding(int index, OnnxMl.StringStringEntryProto value) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.ModelProto.Builder.setMetadataProps(int index, OnnxMl.StringStringEntryProto value) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.setQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto value) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.setUpdateBinding(int index, OnnxMl.StringStringEntryProto value) Gradient-based training is usually an iterative procedure.Method parameters in ai.onnx.proto with type arguments of type OnnxMl.StringStringEntryProtoModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.addAllExternalData(Iterable<? extends OnnxMl.StringStringEntryProto> values) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.addAllInitializationBinding(Iterable<? extends OnnxMl.StringStringEntryProto> values) This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.OnnxMl.ModelProto.Builder.addAllMetadataProps(Iterable<? extends OnnxMl.StringStringEntryProto> values) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.addAllQuantParameterTensorNames(Iterable<? extends OnnxMl.StringStringEntryProto> values) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.addAllUpdateBinding(Iterable<? extends OnnxMl.StringStringEntryProto> values) Gradient-based training is usually an iterative procedure.