Package onnx

Class OnnxMl.TrainingInfoProto

  • All Implemented Interfaces:
    Serializable, OnnxMl.TrainingInfoProtoOrBuilder, org.nd4j.shade.protobuf.Message, org.nd4j.shade.protobuf.MessageLite, org.nd4j.shade.protobuf.MessageLiteOrBuilder, org.nd4j.shade.protobuf.MessageOrBuilder
    Enclosing class:
    OnnxMl
    public static final class OnnxMl.TrainingInfoProto
extends org.nd4j.shade.protobuf.GeneratedMessageV3
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
    See Also:
    Serialized Form

    • Field Detail

      • INITIALIZATION_FIELD_NUMBER

        public static final int INITIALIZATION_FIELD_NUMBER
        See Also:
        Constant Field Values

      • INITIALIZATION_BINDING_FIELD_NUMBER

        public static final int INITIALIZATION_BINDING_FIELD_NUMBER
        See Also:
        Constant Field Values

      • UPDATE_BINDING_FIELD_NUMBER

        public static final int UPDATE_BINDING_FIELD_NUMBER
        See Also:
        Constant Field Values

    • Method Detail

      • newInstance

        protected Object newInstance​(org.nd4j.shade.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
        Overrides:
        newInstance in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • getUnknownFields

        public final org.nd4j.shade.protobuf.UnknownFieldSet getUnknownFields()
        Specified by:
        getUnknownFields in interface org.nd4j.shade.protobuf.MessageOrBuilder
        Overrides:
        getUnknownFields in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • getDescriptor

        public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor()

      • internalGetFieldAccessorTable

        protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • 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.
        .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.
        .onnx.GraphProto initialization = 1;
        Specified by:
        getInitialization in interface OnnxMl.TrainingInfoProtoOrBuilder
        Returns:
        The initialization.

      • 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.
        .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.
        .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.
        .onnx.GraphProto algorithm = 2;
        Specified by:
        getAlgorithm in interface OnnxMl.TrainingInfoProtoOrBuilder
        Returns:
        The algorithm.

      • 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.
        .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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • 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

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface org.nd4j.shade.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • writeTo

        public void writeTo​(org.nd4j.shade.protobuf.CodedOutputStream output)
             throws IOException
        Specified by:
        writeTo in interface org.nd4j.shade.protobuf.MessageLite
        Overrides:
        writeTo in class org.nd4j.shade.protobuf.GeneratedMessageV3
        Throws:
        IOException

      • getSerializedSize

        public int getSerializedSize()
        Specified by:
        getSerializedSize in interface org.nd4j.shade.protobuf.MessageLite
        Overrides:
        getSerializedSize in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • equals

        public boolean equals​(Object obj)
        Specified by:
        equals in interface org.nd4j.shade.protobuf.Message
        Overrides:
        equals in class org.nd4j.shade.protobuf.AbstractMessage

      • hashCode

        public int hashCode()
        Specified by:
        hashCode in interface org.nd4j.shade.protobuf.Message
        Overrides:
        hashCode in class org.nd4j.shade.protobuf.AbstractMessage

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(ByteBuffer data)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(ByteBuffer data,
                                                 org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(org.nd4j.shade.protobuf.ByteString data)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(org.nd4j.shade.protobuf.ByteString data,
                                                 org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(byte[] data)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static OnnxMl.TrainingInfoProto parseFrom​(byte[] data,
                                                 org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
                                          throws org.nd4j.shade.protobuf.InvalidProtocolBufferException
        Throws:
        org.nd4j.shade.protobuf.InvalidProtocolBufferException

      • newBuilderForType

        public OnnxMl.TrainingInfoProto.Builder newBuilderForType()
        Specified by:
        newBuilderForType in interface org.nd4j.shade.protobuf.Message
        Specified by:
        newBuilderForType in interface org.nd4j.shade.protobuf.MessageLite

      • toBuilder

        public OnnxMl.TrainingInfoProto.Builder toBuilder()
        Specified by:
        toBuilder in interface org.nd4j.shade.protobuf.Message
        Specified by:
        toBuilder in interface org.nd4j.shade.protobuf.MessageLite

      • newBuilderForType

        protected OnnxMl.TrainingInfoProto.Builder newBuilderForType​(org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent)
        Specified by:
        newBuilderForType in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • getParserForType

        public org.nd4j.shade.protobuf.Parser<OnnxMl.TrainingInfoProto> getParserForType()
        Specified by:
        getParserForType in interface org.nd4j.shade.protobuf.Message
        Specified by:
        getParserForType in interface org.nd4j.shade.protobuf.MessageLite
        Overrides:
        getParserForType in class org.nd4j.shade.protobuf.GeneratedMessageV3

      • getDefaultInstanceForType

        public OnnxMl.TrainingInfoProto getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface org.nd4j.shade.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface org.nd4j.shade.protobuf.MessageOrBuilder