Cloud AutoML V1beta1 Client - Class ImageClassificationModelMetadata (1.5.4)

Version 1.5.4

• 2.0.2 (latest)
• 2.0.1
• 1.6.5
• 1.5.4
• 1.4.17

Beta

This library is covered by the Pre-GA Offerings Terms of the Terms of Service. Pre-GA libraries might have limited support, and changes to pre-GA libraries might not be compatible with other pre-GA versions. For more information, see the launch stage descriptions.

Reference documentation and code samples for the Cloud AutoML V1beta1 Client class ImageClassificationModelMetadata.

Model metadata for image classification.

Generated from protobuf message google.cloud.automl.v1beta1.ImageClassificationModelMetadata

Namespace

Google \ Cloud \ AutoMl \ V1beta1

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ base_model_id	string Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.
↳ train_budget	int|string Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.
↳ train_cost	int|string Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a base model, the train cost used to create the base model are not included.
↳ stop_reason	string Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.
↳ model_type	string Optional. Type of the model. The available values are: * cloud - Model to be used via prediction calls to AutoML API. This is the default value. * mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models. * mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. * mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models. * mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models. * mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. * mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
↳ node_qps	float Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.
↳ node_count	int|string Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

getBaseModelId

Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.

Returns
Type	Description
string

setBaseModelId

Optional. The ID of the base model. If it is specified, the new model will be created based on the base model. Otherwise, the new model will be created from scratch. The base model must be in the same project and location as the new model to create, and have the same model_type.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getTrainBudget

Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.

Returns
Type	Description
int|string

setTrainBudget

Required. The train budget of creating this model, expressed in hours. The actual train_cost will be equal or less than this value.

Parameter
Name	Description
var	int|string

Returns
Type	Description
$this

getTrainCost

Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a base model, the train cost used to create the base model are not included.

Returns
Type	Description
int|string

setTrainCost

Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a base model, the train cost used to create the base model are not included.

Parameter
Name	Description
var	int|string

Returns
Type	Description
$this

getStopReason

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

Returns
Type	Description
string

setStopReason

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getModelType

Optional. Type of the model. The available values are:

• cloud - Model to be used via prediction calls to AutoML API.

This is the default value.

• mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
• mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
• mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards.
• mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

Returns
Type	Description
string

setModelType

Optional. Type of the model. The available values are:

• cloud - Model to be used via prediction calls to AutoML API.

This is the default value.

• mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
• mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.
• mobile-core-ml-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have low latency, but may have lower prediction quality than other models.
• mobile-core-ml-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards.
• mobile-core-ml-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile device with Core ML afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getNodeQps

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Returns
Type	Description
float

setNodeQps

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

Parameter
Name	Description
var	float

Returns
Type	Description
$this

getNodeCount

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

Returns
Type	Description
int|string

setNodeCount

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field.

Parameter
Name	Description
var	int|string

Returns
Type	Description
$this