ClassificationNeuralNetwork Predict
Libraries:
Statistics and Machine Learning Toolbox /
Classification
Description
The ClassificationNeuralNetwork Predict block classifies observations using
a neural network classification object (ClassificationNeuralNetwork
or CompactClassificationNeuralNetwork
) for multiclass classification.
Import a trained classification object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port label returns a predicted class label for the observation. You can add the optional output port score, which returns predicted class scores or posterior probabilities.
Examples
Predict Class Labels Using ClassificationNeuralNetwork Predict Block
Train a neural network classification model, and then use the ClassificationNeuralNetwork Predict block for label prediction.
Ports
Input
x — Predictor data
row vector | column vector
Predictor data, specified as a row or column vector of one observation.
The variables in x must have the same order as the predictor variables that trained the model specified by Select trained machine learning model.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Output
label — Predicted class label
scalar
Predicted class label, returned as a scalar. The predicted class is the class that
minimizes the expected classification cost. For more details, see the More About section of the
predict
function reference page.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
| enumerated
score — Predicted class scores or posterior probabilities
row vector
Predicted class scores or posterior probabilities, returned as a row vector of
size 1-by-k, where k is the number of classes in
the neural network model. The classification score Score(i)
represents the posterior probability that the observation in x
belongs to class i
.
To check the order of the classes, use the ClassNames
property of the neural network model specified by Select trained machine
learning model.
Dependencies
To enable this port, select the check box for Add output port for predicted class scores on the Main tab of the Block Parameters dialog box.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Parameters
Main
Select trained machine learning model — Neural network classification model
nnetMdl
(default) | ClassificationNeuralNetwork
object | CompactClassificationNeuralNetwork
object
Specify the name of a workspace variable that contains a ClassificationNeuralNetwork
object or CompactClassificationNeuralNetwork
object.
When you train the model by using fitcnet
,
the following restrictions apply:
The predictor data cannot include categorical predictors (
logical
,categorical
,char
,string
, orcell
). If you supply training data in a table, the predictors must be numeric (double
orsingle
). Also, you cannot use theCategoricalPredictors
name-value argument. To include categorical predictors in a model, preprocess them by usingdummyvar
before fitting the model.The value of the
ScoreTransform
name-value argument cannot be'invlogit'
or an anonymous function.
Programmatic Use
Block Parameter:
TrainedLearner |
Type: workspace variable |
Values:
ClassificationNeuralNetwork object |
CompactClassificationNeuralNetwork object |
Default:
'nnetMdl' |
Add output port for predicted class scores — Add second output port for predicted class scores
off
(default) | on
Select the check box to include the second output port score in the ClassificationNeuralNetwork Predict block.
Programmatic Use
Block Parameter:
ShowOutputScore |
Type: character vector |
Values:
'off' | 'on' |
Default:
'off' |
Data Types
Fixed-Point Operational ParametersInteger rounding mode — Rounding mode for fixed-point operations
Floor
(default) | Ceiling
| Convergent
| Nearest
| Round
| Simplest
| Zero
Specify the rounding mode for fixed-point operations. For more information, see Rounding Modes (Fixed-Point Designer).
Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression into the mask field using a MATLAB® rounding function.
Programmatic Use
Block Parameter:
RndMeth |
Type: character vector |
Values:
"Ceiling" | "Convergent" | "Floor" | "Nearest" | "Round" | "Simplest" |
"Zero" |
Default:
"Floor" |
Saturate on integer overflow — Method of overflow action
off
(default) | on
Specify whether overflows saturate or wrap.
Action | Rationale | Impact on Overflows | Example |
---|---|---|---|
Select this check box
( | Your model has possible overflow, and you want explicit saturation protection in the generated code. | Overflows saturate to either the minimum or maximum value that the data type can represent. | The maximum value that the |
Clear this check box
( | You want to optimize the efficiency of your generated code. You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink). | Overflows wrap to the appropriate value that the data type can represent. | The maximum value that the |
Programmatic Use
Block Parameter:
SaturateOnIntegerOverflow |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type
off
(default) | on
Select this parameter to prevent the fixed-point tools from overriding the data type you specify for the block. For more information, see Use Lock Output Data Type Setting (Fixed-Point Designer).
Programmatic Use
Block Parameter:
LockScale |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Label data type — Data type of label output
Inherit: Inherit via back propagation
| Inherit: auto
| double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| Enum: <class name>
| <data type expression>
Specify the data type for the label output. The type can be
inherited, specified as an enumerated data type, or
expressed as a data type object such as Simulink.NumericType
.
The supported data types depend on the labels used in the model specified by Select trained machine learning model.
If the model uses numeric or logical labels, the supported data types are
Inherit: Inherit via back propagation
(default),double
,single
,half
,int8
,uint8
,int16
,uint16
,int32
,uint32
,int64
,uint64
,boolean
, fixed point, and a data type object.If the model uses nonnumeric labels, the supported data types are
Inherit: auto
(default),Enum: <class name>
, and a data type object.
When you select an inherited option, the software behaves as follows:
Inherit: Inherit via back propagation
(default for numeric and logical labels) — Simulink® automatically determines the Label data type of the block during data type propagation (see Data Type Propagation (Simulink)). In this case, the block uses the data type of a downstream block or signal object.Inherit: auto
(default for nonnumeric labels) — The block uses an autodefined enumerated data type variable. For example, suppose the workspace variable name specified by Select trained machine learning model ismyMdl
, and the class labels areclass 1
andclass 2
. Then, the corresponding label values aremyMdl_enumLabels.class_1
andmyMdl_enumLabels.class_2
. The block converts the class labels to valid MATLAB identifiers by using thematlab.lang.makeValidName
function.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
LabelDataTypeStr |
Type: character vector |
Values: "Inherit: Inherit via back
propagation" | "Inherit: auto" |
"double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| "Enum: <class name>" | "<data type
expression>" |
Default: "Inherit: Inherit via
back propagation" (for numeric and logical labels) |
"Inherit: auto" (for nonnumeric labels) |
Label data type Minimum — Minimum value of label output for range checking
[]
(default) | scalar
Specify the lower value of the label output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Label data type Minimum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.
Dependencies
You can specify this parameter only if the model specified by Select trained machine learning model uses numeric labels.
Programmatic Use
Block Parameter:
LabelOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Label data type Maximum — Maximum value of label output for range checking
[]
(default) | scalar
Specify the upper value of the label output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Label data type Maximum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.
Dependencies
You can specify this parameter only if the model specified by Select trained machine learning model uses numeric labels.
Programmatic Use
Block Parameter:
LabelOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Score data type — Data type of score output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the score output. The type can be
inherited, specified directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that
inherits a data
type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
ScoreDataTypeStr |
Type: character vector |
Values: "Inherit: auto"
| "double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| "<data type expression>" |
Default: "Inherit:
auto" |
Score data type Minimum — Minimum value of score output for range checking
[]
(default) | scalar
Specify the lower value of the score output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Score data type Minimum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
ScoreOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Score data type Maximum — Maximum value of score output for range checking
[]
(default) | scalar
Specify the upper value of the score output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Score data type Maximum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
ScoreOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Raw score data type — Untransformed score data type
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the internal untransformed scores. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that inherits a data type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Dependencies
You can specify this parameter only if the model specified by Select trained
machine learning model uses a score transformation other than
"none"
(default, same as "identity"
).
If the model uses no score transformations (
"none"
or"identity"
), then you can specify the score data type by using Score data type.If the model uses a score transformation other than
"none"
or"identity"
, then you can specify the data type of untransformed raw scores by using this parameter. To specify the data type of transformed scores, use Score data type.
You can change the score transformation option by specifying the
ScoreTransform
name-value argument during training, or by
modifying the ScoreTransform
property after training.
Programmatic Use
Block Parameter: RawScoreDataTypeStr |
Type: character vector |
Values: "Inherit: auto" |
"double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| "<data type expression>" |
Default: "Inherit: auto" |
Raw score data type Minimum — Minimum untransformed score for range checking
[]
(default) | scalar
Specify the lower value of the untransformed score range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Raw score data type Minimum parameter does not saturate or clip the actual untransformed score signal.
Programmatic Use
Block Parameter:
RawScoreOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Raw score data type Maximum — Maximum untransformed score for range checking
[]
(default) | scalar
Specify the upper value of the untransformed score range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Raw score data type Maximum parameter does not saturate or clip the actual untransformed score signal.
Programmatic Use
Block Parameter:
RawScoreOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Output layer data type — Data type of final fully connected layer
Inherit: Inherit via internal rule
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the output layer. The type can be inherited, specified
directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: Inherit via internal rule
, the
block uses an internal rule to determine the output data type. The internal rule chooses
a data type that optimizes numerical accuracy, performance, and generated code size,
while taking into account the properties of the embedded target hardware. The software
cannot always optimize efficiency and numerical accuracy at the same
time.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
OutputLayerDataTypeStr |
Type: character vector |
Values: 'Inherit: Inherit via
internal rule' | 'double' |
'single' | 'half' |
'int8' | 'uint8' |
'int16' | 'uint16' |
'int32' | 'uint32' |
'int64' | 'uint64' |
'boolean' | 'fixdt(1,16,0)' |
'fixdt(1,16,2^0,0)' | '<data type
expression>' |
Default: 'Inherit: Inherit via
internal rule' |
Output layer data type Minimum — Minimum value for final fully connected layer
[]
(default) | scalar
Specify the lower value of the output layer's internal variable range checked by Simulink.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output layer data type Minimum parameter does not saturate or clip the output layer value signal.
Programmatic Use
Block Parameter:
OutputLayerOutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Output layer data type Maximum — Maximum value for final fully connected layer
[]
(default) | scalar
Specify the upper value of the output layer's internal variable range checked by Simulink.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output layer data type Maximum parameter does not saturate or clip the output layer value signal.
Programmatic Use
Block Parameter:
OutputLayerOutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Layer 1 data type — Data type of first fully connected layer
Inherit: Inherit via internal rule
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the first layer. The type can be inherited, specified
directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: Inherit via internal rule
, the
block uses an internal rule to determine the data type. The internal rule chooses a data
type that optimizes numerical accuracy, performance, and generated code size, while
taking into account the properties of the embedded target hardware. The software cannot
always optimize efficiency and numerical accuracy at the same
time.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Tips
A trained neural network can have more than one fully connected layer, excluding the output layer.
You can specify the data type for each individual layer for the first 10 layers. Specify the data type Layer n data type for each layer. The data type of the first layer is Layer 1 data type, the data type of the second layer is Layer 2 data type, and so on.
You can specify the data type for layers 11 to k, where k is the total number of layers, by using the data type Additional layer(s) data type. The Block Parameter for Additional layer(s) data type is
Layer11DataTypeStr
.The data types Layer n data type and Additional layer(s) data type can be inherited, specified directly, or expressed as a data type object such as
Simulink.NumericType
. These data types support the same values as Layer 1 data type.
Programmatic Use
Block Parameter:
Layer1DataTypeStr |
Type: character vector |
Values: 'Inherit: Inherit via
internal rule' | 'double' |
'single' | 'half' |
'int8' | 'uint8' |
'int16' | 'uint16' |
'int32' | 'uint32' |
'int64' | 'uint64' |
'boolean' | 'fixdt(1,16,0)' |
'fixdt(1,16,2^0,0)' | '<data type
expression>' |
Default: 'Inherit: Inherit via
internal rule' |
Layer 1 data type Minimum — Minimum value for first fully connected layer
[]
(default) | scalar
Specify the lower value of the first layer's internal variable range checked by Simulink.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Layer 1 data type Minimum parameter does not saturate or clip the first layer value signal.
Tips
A trained neural network can have more than one fully connected layer, excluding the output layer.
You can specify the lower value of each individual layer's internal variable range checked by Simulink for the first 10 layers. Specify the lower value Layer n minimum for each layer. The minimum value of the first layer is Layer 1 minimum, the minimum value of the second layer is Layer 2 minimum, and so on.
You can specify the lower value for layers 11 to k, where k is the total number of layers, by using Additional layer(s) minimum. The Block Parameter for Additional layer(s) minimum is
Layer11OutMin
.Layer n minimum and Additional layer(s) minimum support the same values as Layer 1 minimum.
Programmatic Use
Block Parameter:
Layer1OutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Layer 1 data type Maximum — Maximum value for first fully connected layer
[]
(default) | scalar
Specify the upper value of the first layer's internal variable range checked by Simulink.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Layer 1 data type Maximum parameter does not saturate or clip the first layer value signal.
Tips
A trained neural network can have more than one fully connected layer, excluding the output layer.
You can specify the upper value of each individual layer's internal variable range checked by Simulink for the first 10 layers. Specify the upper value Layer n maximum for each layer. The maximum value of the first layer is Layer 1 maximum, the maximum value of the second layer is Layer 2 maximum, and so on.
You can specify the upper value for layers 11 to k, where k is the total number of layers, by using Additional layer(s) maximum. The Block Parameter for Additional layer(s) maximum is
Layer11OutMax
.Layer n maximum and Additional layer(s) maximum support the same values as Layer 1 maximum.
Programmatic Use
Block Parameter:
Layer1OutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
Alternative Functionality
You can use a MATLAB Function block with the predict
object
function of a neural network classification object (ClassificationNeuralNetwork
or CompactClassificationNeuralNetwork
). For an example, see Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the ClassificationNeuralNetwork Predict block
in the Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict
function, consider the
following:
If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
Support for variable-size arrays must be enabled for a MATLAB Function block with the
predict
function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.
Version History
Introduced in R2021b
See Also
Blocks
- ClassificationSVM Predict | ClassificationEnsemble Predict | ClassificationTree Predict | RegressionNeuralNetwork Predict
Objects
Functions
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