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.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ base_model_id |
string
Optional. The ID of the |
↳ 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_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. |
↳ model_type |
string
Optional. Type of the model. The available values are: * |
↳ 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 |
|
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.
| Returns | |
|---|---|
| Type | Description |
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.
| Parameter | |
|---|---|
| Name | Description |
var |
int|string
|
| Returns | |
|---|---|
| Type | Description |
$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.
| Returns | |
|---|---|
| Type | Description |
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.
| 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 |
|