Reference documentation and code samples for the Cloud AutoML V1 Client class ImageClassificationModelMetadata.
Model metadata for image classification.
Generated from protobuf message google.cloud.automl.v1.ImageClassificationModelMetadata
Namespace
Google \ Cloud \ AutoMl \ V1Methods
__construct
Constructor.
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_milli_node_hours
int|string
Optional. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost
will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED
. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud
(default), the train 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. For model type mobile-low-latency-1
, mobile-versatile-1
, mobile-high-accuracy-1
, mobile-core-ml-low-latency-1
, mobile-core-ml-versatile-1
, mobile-core-ml-high-accuracy-1
, the train 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.
↳ train_cost_milli_node_hours
int|string
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.
↳ 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
.
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
.
var
string
$this
getTrainBudgetMilliNodeHours
Optional. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost
will be equal or less than this value. If further model
training ceases to provide any improvements, it will stop without using
full budget and the stop_reason will be MODEL_CONVERGED
.
Note, node_hour = actual_hour * number_of_nodes_invovled.
For model type cloud
(default), the train 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. For model type mobile-low-latency-1
, mobile-versatile-1
, mobile-high-accuracy-1
, mobile-core-ml-low-latency-1
, mobile-core-ml-versatile-1
, mobile-core-ml-high-accuracy-1
, the train 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.
int|string
setTrainBudgetMilliNodeHours
Optional. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost
will be equal or less than this value. If further model
training ceases to provide any improvements, it will stop without using
full budget and the stop_reason will be MODEL_CONVERGED
.
Note, node_hour = actual_hour * number_of_nodes_invovled.
For model type cloud
(default), the train 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. For model type mobile-low-latency-1
, mobile-versatile-1
, mobile-high-accuracy-1
, mobile-core-ml-low-latency-1
, mobile-core-ml-versatile-1
, mobile-core-ml-high-accuracy-1
, the train 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.
var
int|string
$this
getTrainCostMilliNodeHours
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour.
Guaranteed to not exceed the train budget.
int|string
setTrainCostMilliNodeHours
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour.
Guaranteed to not exceed the train budget.
var
int|string
$this
getStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED
, MODEL_CONVERGED
.
string
setStopReason
Output only. The reason that this create model operation stopped,
e.g. BUDGET_REACHED
, MODEL_CONVERGED
.
var
string
$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.
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.
var
string
$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.
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.
var
float
$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.
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.
var
int|string
$this
