Class OnnxMl.TrainingInfoProto.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<OnnxMl.TrainingInfoProto.Builder>
com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>
ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
All Implemented Interfaces:
OnnxMl.TrainingInfoProtoOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
OnnxMl.TrainingInfoProto
public static final class OnnxMl.TrainingInfoProto.Builder extends com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder> implements OnnxMl.TrainingInfoProtoOrBuilder
 Training information
 TrainingInfoProto stores information for training a model.
 In particular, this defines two functionalities: an initialization-step
 and a training-algorithm-step. Initialization resets the model
 back to its original state as if no training has been performed.
 Training algorithm improves the model based on input data.
 The semantics of the initialization-step is that the initializers
 in ModelProto.graph and in TrainingInfoProto.algorithm are first
 initialized as specified by the initializers in the graph, and then
 updated by the "initialization_binding" in every instance in
 ModelProto.training_info.
 The field "algorithm" defines a computation graph which represents a
 training algorithm's step. After the execution of a
 TrainingInfoProto.algorithm, the initializers specified by "update_binding"
 may be immediately updated. If the targeted training algorithm contains
 consecutive update steps (such as block coordinate descent methods),
 the user needs to create a TrainingInfoProto for each step.
Protobuf type onnx.TrainingInfoProto

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • clear

      public OnnxMl.TrainingInfoProto.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • getDefaultInstanceForType

      public OnnxMl.TrainingInfoProto getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public OnnxMl.TrainingInfoProto build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public OnnxMl.TrainingInfoProto buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • clone

      public OnnxMl.TrainingInfoProto.Builder clone()
      Specified by:
      clone in interface com.google.protobuf.Message.Builder
      Specified by:
      clone in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clone in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • setField

      public OnnxMl.TrainingInfoProto.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
      Specified by:
      setField in interface com.google.protobuf.Message.Builder
      Overrides:
      setField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • clearField

      public OnnxMl.TrainingInfoProto.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
      Specified by:
      clearField in interface com.google.protobuf.Message.Builder
      Overrides:
      clearField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • clearOneof

      public OnnxMl.TrainingInfoProto.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
      Specified by:
      clearOneof in interface com.google.protobuf.Message.Builder
      Overrides:
      clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • setRepeatedField

      public OnnxMl.TrainingInfoProto.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
      Specified by:
      setRepeatedField in interface com.google.protobuf.Message.Builder
      Overrides:
      setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • addRepeatedField

      public OnnxMl.TrainingInfoProto.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
      Specified by:
      addRepeatedField in interface com.google.protobuf.Message.Builder
      Overrides:
      addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • mergeFrom

      public OnnxMl.TrainingInfoProto.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<OnnxMl.TrainingInfoProto.Builder>

    • mergeFrom

      public OnnxMl.TrainingInfoProto.Builder mergeFrom(OnnxMl.TrainingInfoProto other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • mergeFrom

      public OnnxMl.TrainingInfoProto.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<OnnxMl.TrainingInfoProto.Builder>
      Throws:
      IOException

    • hasInitialization

      public boolean hasInitialization()
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;
      Specified by:
      hasInitialization in interface OnnxMl.TrainingInfoProtoOrBuilder
      Returns:
      Whether the initialization field is set.

    • getInitialization

      public OnnxMl.GraphProto getInitialization()
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;
      Specified by:
      getInitialization in interface OnnxMl.TrainingInfoProtoOrBuilder
      Returns:
      The initialization.

    • setInitialization

      public OnnxMl.TrainingInfoProto.Builder setInitialization(OnnxMl.GraphProto value)
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;

    • setInitialization

      public OnnxMl.TrainingInfoProto.Builder setInitialization(OnnxMl.GraphProto.Builder builderForValue)
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;

    • mergeInitialization

      public OnnxMl.TrainingInfoProto.Builder mergeInitialization(OnnxMl.GraphProto value)
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;

    • clearInitialization

      public OnnxMl.TrainingInfoProto.Builder clearInitialization()
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;

    • getInitializationBuilder

      public OnnxMl.GraphProto.Builder getInitializationBuilder()
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;

    • getInitializationOrBuilder

      public OnnxMl.GraphProtoOrBuilder getInitializationOrBuilder()
       This field describes a graph to compute the initial tensors
 upon starting the training process. Initialization graph has no input
 and can have multiple outputs. Usually, trainable tensors in neural
 networks are randomly initialized. To achieve that, for each tensor,
 the user can put a random number operator such as RandomNormal or
 RandomUniform in TrainingInfoProto.initialization.node and assign its
 random output to the specific tensor using "initialization_binding".
 This graph can also set the initializers in "algorithm" in the same
 TrainingInfoProto; a use case is resetting the number of training
 iteration to zero.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Thus, no initializer would be changed by default.
      optional .onnx.GraphProto initialization = 1;
      Specified by:
      getInitializationOrBuilder in interface OnnxMl.TrainingInfoProtoOrBuilder

    • hasAlgorithm

      public boolean hasAlgorithm()
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;
      Specified by:
      hasAlgorithm in interface OnnxMl.TrainingInfoProtoOrBuilder
      Returns:
      Whether the algorithm field is set.

    • getAlgorithm

      public OnnxMl.GraphProto getAlgorithm()
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;
      Specified by:
      getAlgorithm in interface OnnxMl.TrainingInfoProtoOrBuilder
      Returns:
      The algorithm.

    • setAlgorithm

      public OnnxMl.TrainingInfoProto.Builder setAlgorithm(OnnxMl.GraphProto value)
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;

    • setAlgorithm

      public OnnxMl.TrainingInfoProto.Builder setAlgorithm(OnnxMl.GraphProto.Builder builderForValue)
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;

    • mergeAlgorithm

      public OnnxMl.TrainingInfoProto.Builder mergeAlgorithm(OnnxMl.GraphProto value)
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;

    • clearAlgorithm

      public OnnxMl.TrainingInfoProto.Builder clearAlgorithm()
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;

    • getAlgorithmBuilder

      public OnnxMl.GraphProto.Builder getAlgorithmBuilder()
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;

    • getAlgorithmOrBuilder

      public OnnxMl.GraphProtoOrBuilder getAlgorithmOrBuilder()
       This field represents a training algorithm step. Given required inputs,
 it computes outputs to update initializers in its own or inference graph's
 initializer lists. In general, this field contains loss node, gradient node,
 optimizer node, increment of iteration count.
 An execution of the training algorithm step is performed by executing the
 graph obtained by combining the inference graph (namely "ModelProto.graph")
 and the "algorithm" graph. That is, the actual the actual
 input/initializer/output/node/value_info/sparse_initializer list of
 the training graph is the concatenation of
 "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer"
 and "algorithm.input/initializer/output/node/value_info/sparse_initializer"
 in that order. This combined graph must satisfy the normal ONNX conditions.
 Now, let's provide a visualization of graph combination for clarity.
 Let the inference graph (i.e., "ModelProto.graph") be
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d
 and the "algorithm" graph be
    tensor_d -> Add -> tensor_e
 The combination process results
    tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e
 Notice that an input of a node in the "algorithm" graph may reference the
 output of a node in the inference graph (but not the other way round). Also, inference
 node cannot reference inputs of "algorithm". With these restrictions, inference graph
 can always be run independently without training information.
 By default, this field is an empty graph and its evaluation does not
 produce any output. Evaluating the default training step never
 update any initializers.
      optional .onnx.GraphProto algorithm = 2;
      Specified by:
      getAlgorithmOrBuilder in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getInitializationBindingList

      public List<OnnxMl.StringStringEntryProto> 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;
      Specified by:
      getInitializationBindingList in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getInitializationBindingCount

      public int getInitializationBindingCount()
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;
      Specified by:
      getInitializationBindingCount in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getInitializationBinding

      public OnnxMl.StringStringEntryProto 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;
      Specified by:
      getInitializationBinding in interface OnnxMl.TrainingInfoProtoOrBuilder

    • setInitializationBinding

      public 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • setInitializationBinding

      public OnnxMl.TrainingInfoProto.Builder setInitializationBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addInitializationBinding

      public 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addInitializationBinding

      public 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addInitializationBinding

      public OnnxMl.TrainingInfoProto.Builder addInitializationBinding(OnnxMl.StringStringEntryProto.Builder builderForValue)
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addInitializationBinding

      public OnnxMl.TrainingInfoProto.Builder addInitializationBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addAllInitializationBinding

      public 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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • clearInitializationBinding

      public OnnxMl.TrainingInfoProto.Builder clearInitializationBinding()
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • removeInitializationBinding

      public OnnxMl.TrainingInfoProto.Builder removeInitializationBinding(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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • getInitializationBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder getInitializationBindingBuilder(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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • getInitializationBindingOrBuilder

      public OnnxMl.StringStringEntryProtoOrBuilder getInitializationBindingOrBuilder(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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;
      Specified by:
      getInitializationBindingOrBuilder in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getInitializationBindingOrBuilderList

      public List<? extends OnnxMl.StringStringEntryProtoOrBuilder> getInitializationBindingOrBuilderList()
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;
      Specified by:
      getInitializationBindingOrBuilderList in interface OnnxMl.TrainingInfoProtoOrBuilder

    • addInitializationBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder addInitializationBindingBuilder()
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • addInitializationBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder addInitializationBindingBuilder(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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • getInitializationBindingBuilderList

      public List<OnnxMl.StringStringEntryProto.Builder> getInitializationBindingBuilderList()
       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.
 See "update_binding" below for details.
 By default, this field is empty and no initializer would be changed
 by the execution of "initialization".
      repeated .onnx.StringStringEntryProto initialization_binding = 3;

    • getUpdateBindingList

      public List<OnnxMl.StringStringEntryProto> getUpdateBindingList()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;
      Specified by:
      getUpdateBindingList in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getUpdateBindingCount

      public int getUpdateBindingCount()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;
      Specified by:
      getUpdateBindingCount in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getUpdateBinding

      public OnnxMl.StringStringEntryProto getUpdateBinding(int index)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;
      Specified by:
      getUpdateBinding in interface OnnxMl.TrainingInfoProtoOrBuilder

    • setUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder setUpdateBinding(int index, OnnxMl.StringStringEntryProto value)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • setUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder setUpdateBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder addUpdateBinding(OnnxMl.StringStringEntryProto value)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder addUpdateBinding(int index, OnnxMl.StringStringEntryProto value)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder addUpdateBinding(OnnxMl.StringStringEntryProto.Builder builderForValue)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder addUpdateBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addAllUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder addAllUpdateBinding(Iterable<? extends OnnxMl.StringStringEntryProto> values)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • clearUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder clearUpdateBinding()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • removeUpdateBinding

      public OnnxMl.TrainingInfoProto.Builder removeUpdateBinding(int index)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • getUpdateBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder getUpdateBindingBuilder(int index)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • getUpdateBindingOrBuilder

      public OnnxMl.StringStringEntryProtoOrBuilder getUpdateBindingOrBuilder(int index)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;
      Specified by:
      getUpdateBindingOrBuilder in interface OnnxMl.TrainingInfoProtoOrBuilder

    • getUpdateBindingOrBuilderList

      public List<? extends OnnxMl.StringStringEntryProtoOrBuilder> getUpdateBindingOrBuilderList()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;
      Specified by:
      getUpdateBindingOrBuilderList in interface OnnxMl.TrainingInfoProtoOrBuilder

    • addUpdateBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder addUpdateBindingBuilder()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • addUpdateBindingBuilder

      public OnnxMl.StringStringEntryProto.Builder addUpdateBindingBuilder(int index)
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • getUpdateBindingBuilderList

      public List<OnnxMl.StringStringEntryProto.Builder> getUpdateBindingBuilderList()
       Gradient-based training is usually an iterative procedure. In one gradient
 descent iteration, we apply
 x = x - r * g
 where "x" is the optimized tensor, "r" stands for learning rate, and "g" is
 gradient of "x" with respect to a chosen loss. To avoid adding assignments
 into the training graph, we split the update equation into
 y = x - r * g
 x = y
 The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To
 tell that "y" should be assigned to "x", the field "update_binding" may
 contain a key-value pair of strings, "x" (key of StringStringEntryProto)
 and "y" (value of StringStringEntryProto).
 For a neural network with multiple trainable (mutable) tensors, there can
 be multiple key-value pairs in "update_binding".
 The initializers appears as keys in "update_binding" are considered
 mutable variables. This implies some behaviors
 as described below.
  1. We have only unique keys in all "update_binding"s so that two
     variables may not have the same name. This ensures that one
     variable is assigned up to once.
  2. The keys must appear in names of "ModelProto.graph.initializer" or
     "TrainingInfoProto.algorithm.initializer".
  3. The values must be output names of "algorithm" or "ModelProto.graph.output".
  4. Mutable variables are initialized to the value specified by the
     corresponding initializer, and then potentially updated by
     "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s.
 This field usually contains names of trainable tensors
 (in ModelProto.graph), optimizer states such as momentums in advanced
 stochastic gradient methods (in TrainingInfoProto.graph),
 and number of training iterations (in TrainingInfoProto.graph).
 By default, this field is empty and no initializer would be changed
 by the execution of "algorithm".
      repeated .onnx.StringStringEntryProto update_binding = 4;

    • setUnknownFields

      public final OnnxMl.TrainingInfoProto.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
      Specified by:
      setUnknownFields in interface com.google.protobuf.Message.Builder
      Overrides:
      setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>

    • mergeUnknownFields

      public final OnnxMl.TrainingInfoProto.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
      Specified by:
      mergeUnknownFields in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.TrainingInfoProto.Builder>