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Trainer
Trainer
Class Trainer
Hierarchy
AbstractTrainer
Trainer
Index
Constructors
constructor
Properties
iterations
learning
Rate
network
optimizer
regularization
verbose
verbose
Step
Methods
cost
set
Iterations
set
Learning
Rate
set
Regularization
set
Step
Callback
set
Verbose
set
Verbose
Step
step
Callback
train
Constructors
constructor
new
Trainer
(
network
:
Network
, optimizer
:
AbstractOptimizer
)
:
Trainer
Parameters
network:
Network
optimizer:
AbstractOptimizer
Returns
Trainer
Properties
iterations
iterations
:
number
= 1000
learning
Rate
learning
Rate
:
number
= 0.001
network
network
:
Network
= null
optimizer
optimizer
:
AbstractOptimizer
= null
regularization
regularization
:
number
= 1e-4
verbose
verbose
:
boolean
= true
verbose
Step
verbose
Step
:
number
= 1
Methods
cost
cost
(
X
:
Matrix
, Y
:
Matrix
)
:
CostResult
Parameters
X:
Matrix
Y:
Matrix
Returns
CostResult
set
Iterations
set
Iterations
(
iterations
:
number
)
:
AbstractTrainer
Parameters
iterations:
number
Returns
AbstractTrainer
set
Learning
Rate
set
Learning
Rate
(
learningRate
:
number
)
:
AbstractTrainer
Parameters
learningRate:
number
Returns
AbstractTrainer
set
Regularization
set
Regularization
(
regularization
:
number
)
:
AbstractTrainer
Parameters
regularization:
number
Returns
AbstractTrainer
set
Step
Callback
set
Step
Callback
(
stepCallback
:
(
data
:
StepCallbackParameters
)
=>
void
)
:
AbstractTrainer
Parameters
stepCallback:
(
data
:
StepCallbackParameters
)
=>
void
(
data
:
StepCallbackParameters
)
:
void
Parameters
data:
StepCallbackParameters
Returns
void
Returns
AbstractTrainer
set
Verbose
set
Verbose
(
verbose
:
boolean
)
:
AbstractTrainer
Parameters
verbose:
boolean
Returns
AbstractTrainer
set
Verbose
Step
set
Verbose
Step
(
verboseStep
:
number
)
:
AbstractTrainer
Parameters
verboseStep:
number
Returns
AbstractTrainer
step
Callback
step
Callback
(
data
:
StepCallbackParameters
)
:
void
Parameters
data:
StepCallbackParameters
Returns
void
train
train
(
inputDataset
:
Dataset
, outputDataset
:
Dataset
)
:
AbstractTrainer
Parameters
inputDataset:
Dataset
outputDataset:
Dataset
Returns
AbstractTrainer
Modules
Computation
Computation/
Abstract
Computation
Computation/
ComputationCPU
Computation/
ComputationGPU
Computation/utils
Dataset
Dataset/
Dataset
Dataset/
Dataset
Modifier
Dataset/
Dataset
Modifier/
Abstract
Dataset
Modifier
Dataset/
Dataset
Modifier/
Callback
Dataset/
Dataset
Modifier/
Min
Max
Scaling
Dataset/
Dataset
Modifier/
Missing
Data
Dataset
Builder
Dataset
Builder/
Dataset
Builder
Dataset
Builder/
Dataset
Builder
Source
Dataset
Builder/
Dataset
Builder
Source/
Abstract
Document
Builder
Source
Dataset
Builder/
Dataset
Builder
Source/
Dataset
Builder
SourceCSV
Layer
Layer/
Abstract
Layer
Layer/
Abstract
Layer1D
Layer/
Abstract
Layer3D
Layer/
Backpropagation
Layer/
Backpropagation/
Abstract
Backpropagation
Layer/
Backpropagation/
Backpropagation1
Dto1D
Layer/
Backpropagation/
Backpropagation3
Dto1D
Layer/
Backpropagation/
Backpropagation
Factory
Layer/
Backpropagation/
Backpropagation
To
Conv
Layer/
Backpropagation/
Backpropagation
To
Max
Pool
Layer/
Conv
Layer/
Fully
Connected
Layer/
Logistic
Layer/
Max
Pool
Layer/
Purelin
Layer/
Relu
Layer/
Softmax
Layer/
Softplus
Layer/
Tanh
Math/
Matrix
Math/math
Network/
Network
Network
Builder
Network
Builder/
Abstract
Network
Builder
Network
Builder/
Network
Builder1D
Network
Builder/
Network
Builder3D
Network
Builder/types
Trainer
Trainer/
Abstract
Trainer
Trainer/
Mini
Batch
Trainer
Trainer/
Optimizer
Trainer/
Optimizer/
Abstract
Optimizer
Trainer/
Optimizer/
Optimizer
Adagrad
Trainer/
Optimizer/
Optimizer
Adam
Trainer/
Optimizer/
Optimizer
Gradient
Descent
main
types
Trainer
constructor
iterations
learning
Rate
network
optimizer
regularization
verbose
verbose
Step
cost
set
Iterations
set
Learning
Rate
set
Regularization
set
Step
Callback
set
Verbose
set
Verbose
Step
step
Callback
train
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