public static interface OnnxMl.TrainingInfoProtoOrBuilder
extends com.google.protobuf.MessageOrBuilder
| Modifier and Type | Method and Description |
|---|---|
OnnxMl.GraphProto |
getAlgorithm()
This field represents a training algorithm step.
|
OnnxMl.GraphProtoOrBuilder |
getAlgorithmOrBuilder()
This field represents a training algorithm step.
|
OnnxMl.GraphProto |
getInitialization()
This field describes a graph to compute the initial tensors
upon starting the training process.
|
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.
|
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.
|
java.util.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.
|
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.
|
java.util.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.
|
OnnxMl.GraphProtoOrBuilder |
getInitializationOrBuilder()
This field describes a graph to compute the initial tensors
upon starting the training process.
|
OnnxMl.StringStringEntryProto |
getUpdateBinding(int index)
Gradient-based training is usually an iterative procedure.
|
int |
getUpdateBindingCount()
Gradient-based training is usually an iterative procedure.
|
java.util.List<OnnxMl.StringStringEntryProto> |
getUpdateBindingList()
Gradient-based training is usually an iterative procedure.
|
OnnxMl.StringStringEntryProtoOrBuilder |
getUpdateBindingOrBuilder(int index)
Gradient-based training is usually an iterative procedure.
|
java.util.List<? extends OnnxMl.StringStringEntryProtoOrBuilder> |
getUpdateBindingOrBuilderList()
Gradient-based training is usually an iterative procedure.
|
boolean |
hasAlgorithm()
This field represents a training algorithm step.
|
boolean |
hasInitialization()
This field describes a graph to compute the initial tensors
upon starting the training process.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneofboolean 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;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;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;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;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;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;java.util.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;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;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;java.util.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;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;java.util.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;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;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;java.util.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;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;