public final class AutoMlImageClassificationInputs extends GeneratedMessageV3 implements AutoMlImageClassificationInputsOrBuilder
Protobuf type
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs
Static Fields
public static final int BASE_MODEL_ID_FIELD_NUMBER
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public static final int BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER
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public static final int DISABLE_EARLY_STOPPING_FIELD_NUMBER
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public static final int MODEL_TYPE_FIELD_NUMBER
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public static final int MULTI_LABEL_FIELD_NUMBER
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Static Methods
public static AutoMlImageClassificationInputs getDefaultInstance()
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public static final Descriptors.Descriptor getDescriptor()
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public static AutoMlImageClassificationInputs.Builder newBuilder()
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public static AutoMlImageClassificationInputs.Builder newBuilder(AutoMlImageClassificationInputs prototype)
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public static AutoMlImageClassificationInputs parseDelimitedFrom(InputStream input)
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public static AutoMlImageClassificationInputs parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
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public static AutoMlImageClassificationInputs parseFrom(byte[] data)
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| Name | Description |
| data | byte[]
|
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public static AutoMlImageClassificationInputs parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
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public static AutoMlImageClassificationInputs parseFrom(ByteString data)
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public static AutoMlImageClassificationInputs parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
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public static AutoMlImageClassificationInputs parseFrom(CodedInputStream input)
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public static AutoMlImageClassificationInputs parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
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public static AutoMlImageClassificationInputs parseFrom(InputStream input)
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public static AutoMlImageClassificationInputs parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
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public static AutoMlImageClassificationInputs parseFrom(ByteBuffer data)
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public static AutoMlImageClassificationInputs parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
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public static Parser<AutoMlImageClassificationInputs> parser()
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Methods
public boolean equals(Object obj)
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Overrides
public String getBaseModelId()
The ID of the base model. If it is specified, the new model will be
trained based on the base model. Otherwise, the new model will be
trained from scratch. The base model must be in the same
Project and Location as the new Model to train, and have the same
modelType.
string base_model_id = 2;
| Type | Description |
| String | The baseModelId.
|
public ByteString getBaseModelIdBytes()
The ID of the base model. If it is specified, the new model will be
trained based on the base model. Otherwise, the new model will be
trained from scratch. The base model must be in the same
Project and Location as the new Model to train, and have the same
modelType.
string base_model_id = 2;
| Type | Description |
| ByteString | The bytes for baseModelId.
|
public long getBudgetMilliNodeHours()
The training budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual
metadata.costMilliNodeHours will be equal or less than this value.
If further model training ceases to provide any improvements, it will
stop without using the full budget and the metadata.successfulStopReason
will be model-converged.
Note, node_hour = actual_hour * number_of_nodes_involved.
For modelType cloud(default), the budget must be between 8,000
and 800,000 milli node hours, inclusive. The default value is 192,000
which represents one day in wall time, considering 8 nodes are used.
For model types mobile-tf-low-latency-1, mobile-tf-versatile-1,
mobile-tf-high-accuracy-1, the training budget must be between
1,000 and 100,000 milli node hours, inclusive.
The default value is 24,000 which represents one day in wall time on a
single node that is used.
int64 budget_milli_node_hours = 3;
| Type | Description |
| long | The budgetMilliNodeHours.
|
public AutoMlImageClassificationInputs getDefaultInstanceForType()
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public boolean getDisableEarlyStopping()
Use the entire training budget. This disables the early stopping feature.
When false the early stopping feature is enabled, which means that
AutoML Image Classification might stop training before the entire
training budget has been used.
bool disable_early_stopping = 4;
| Type | Description |
| boolean | The disableEarlyStopping.
|
public AutoMlImageClassificationInputs.ModelType getModelType()
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
public int getModelTypeValue()
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType model_type = 1;
| Type | Description |
| int | The enum numeric value on the wire for modelType.
|
public boolean getMultiLabel()
If false, a single-label (multi-class) Model will be trained (i.e.
assuming that for each image just up to one annotation may be
applicable). If true, a multi-label Model will be trained (i.e.
assuming that for each image multiple annotations may be applicable).
bool multi_label = 5;
| Type | Description |
| boolean | The multiLabel.
|
public Parser<AutoMlImageClassificationInputs> getParserForType()
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public int getSerializedSize()
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public final UnknownFieldSet getUnknownFields()
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protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
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public final boolean isInitialized()
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public AutoMlImageClassificationInputs.Builder newBuilderForType()
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protected AutoMlImageClassificationInputs.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
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protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
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public AutoMlImageClassificationInputs.Builder toBuilder()
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public void writeTo(CodedOutputStream output)
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Exceptions
