Struct rusoto_machinelearning::MLModel
source · [−]pub struct MLModel {Show 19 fields
pub algorithm: Option<String>,
pub compute_time: Option<i64>,
pub created_at: Option<f64>,
pub created_by_iam_user: Option<String>,
pub endpoint_info: Option<RealtimeEndpointInfo>,
pub finished_at: Option<f64>,
pub input_data_location_s3: Option<String>,
pub last_updated_at: Option<f64>,
pub ml_model_id: Option<String>,
pub ml_model_type: Option<String>,
pub message: Option<String>,
pub name: Option<String>,
pub score_threshold: Option<f32>,
pub score_threshold_last_updated_at: Option<f64>,
pub size_in_bytes: Option<i64>,
pub started_at: Option<f64>,
pub status: Option<String>,
pub training_data_source_id: Option<String>,
pub training_parameters: Option<HashMap<String, String>>,
}
Expand description
Represents the output of a GetMLModel
operation.
The content consists of the detailed metadata and the current status of the MLModel
.
Fields
algorithm: Option<String>
The algorithm used to train the MLModel
. The following algorithm is supported:
-
SGD
-- Stochastic gradient descent. The goal ofSGD
is to minimize the gradient of the loss function.
compute_time: Option<i64>
created_at: Option<f64>
The time that the MLModel
was created. The time is expressed in epoch time.
created_by_iam_user: Option<String>
The AWS user account from which the MLModel
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
endpoint_info: Option<RealtimeEndpointInfo>
The current endpoint of the MLModel
.
finished_at: Option<f64>
input_data_location_s3: Option<String>
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
last_updated_at: Option<f64>
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
ml_model_id: Option<String>
The ID assigned to the MLModel
at creation.
ml_model_type: Option<String>
Identifies the MLModel
category. The following are the available types:
-
REGRESSION
- Produces a numeric result. For example, "What price should a house be listed at?" -
BINARY
- Produces one of two possible results. For example, "Is this a child-friendly web site?". -
MULTICLASS
- Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
message: Option<String>
A description of the most recent details about accessing the MLModel
.
name: Option<String>
A user-supplied name or description of the MLModel
.
score_threshold: Option<f32>
score_threshold_last_updated_at: Option<f64>
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
size_in_bytes: Option<i64>
started_at: Option<f64>
status: Option<String>
The current status of an MLModel
. This element can have one of the following values:
-
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to create anMLModel
. -
INPROGRESS
- The creation process is underway. -
FAILED
- The request to create anMLModel
didn't run to completion. The model isn't usable. -
COMPLETED
- The creation process completed successfully. -
DELETED
- TheMLModel
is marked as deleted. It isn't usable.
training_data_source_id: Option<String>
The ID of the training DataSource
. The CreateMLModel
operation uses the TrainingDataSourceId
.
training_parameters: Option<HashMap<String, String>>
A list of the training parameters in the MLModel
. The list is implemented as a map of key-value pairs.
The following is the current set of training parameters:
-
sgd.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.The value is an integer that ranges from
100000
to2147483648
. The default value is33554432
. -
sgd.maxPasses
- The number of times that the training process traverses the observations to build theMLModel
. The value is an integer that ranges from1
to10000
. The default value is10
. -
sgd.shuffleType
- Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values areauto
andnone
. The default value isnone
. -
sgd.l1RegularizationAmount
- The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAXDOUBLE
. The default is to not use L1 normalization. This parameter can't be used whenL2
is specified. Use this parameter sparingly. -
sgd.l2RegularizationAmount
- The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as1.0E-08
.The value is a double that ranges from
0
toMAXDOUBLE
. The default is to not use L2 normalization. This parameter can't be used whenL1
is specified. Use this parameter sparingly.
Trait Implementations
sourceimpl<'de> Deserialize<'de> for MLModel
impl<'de> Deserialize<'de> for MLModel
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
impl StructuralPartialEq for MLModel
Auto Trait Implementations
impl RefUnwindSafe for MLModel
impl Send for MLModel
impl Sync for MLModel
impl Unpin for MLModel
impl UnwindSafe for MLModel
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more