Model (deeplearning4j-core 0.0.3.3 API)

All Known Subinterfaces:

Classifier, Layer

All Known Implementing Classes:

AutoEncoder, BarnesHutTsne, BaseLayer, BasePretrainNetwork, ConvolutionDownSampleLayer, LSTM, MultiLayerNetwork, OutputLayer, RBM, RecursiveAutoEncoder
```
public interface Model
```
A Model is meant for predicting something from data. Note that this is not like supervised learning where there are labels attached to the examples.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`int`	`batchSize()` The current inputs batch size
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`void`	`fit()` All models have a fit method
`void`	`fit(org.nd4j.linalg.api.ndarray.INDArray data)` Fit the model to the given data
`Gradient`	`getGradient()` Calculate a gradient
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`org.nd4j.linalg.api.ndarray.INDArray`	`input()` The input/feature matrix for the model
`void`	`iterate(org.nd4j.linalg.api.ndarray.INDArray input)` Run one iteration
`int`	`numParams()` the number of parameters for the model
`org.nd4j.linalg.api.ndarray.INDArray`	`params()` Parameters of the model (if any)
`double`	`score()` The score for the model
`void`	`setConf(NeuralNetConfiguration conf)` Setter for the configuration
`void`	`setParams(org.nd4j.linalg.api.ndarray.INDArray params)` Set the parameters for this model.
`org.nd4j.linalg.api.ndarray.INDArray`	`transform(org.nd4j.linalg.api.ndarray.INDArray data)` Transform the data based on the model's output.
`void`	`update(Gradient gradient)` Perform one update applying the gradient
`void`	`validateInput()` Validate the input

- Method Detail
  - fit
```
void fit()
```
    All models have a fit method
  - update
```
void update(Gradient gradient)
```
    Perform one update applying the gradient
    
    Parameters:
    
    gradient - the gradient to apply
  - score
```
double score()
```
    The score for the model
    
    Returns:
    
    the score for the model
  - transform
```
org.nd4j.linalg.api.ndarray.INDArray transform(org.nd4j.linalg.api.ndarray.INDArray data)
```
    Transform the data based on the model's output. This can be anything from a number to reconstructions.
    
    Parameters:
    
    data - the data to transform
    
    Returns:
    
    the transformed data
  - params
```
org.nd4j.linalg.api.ndarray.INDArray params()
```
    Parameters of the model (if any)
    
    Returns:
    
    the parameters of the model
  - numParams
```
int numParams()
```
    the number of parameters for the model
    
    Returns:
    
    the number of parameters for the model
  - setParams
```
void setParams(org.nd4j.linalg.api.ndarray.INDArray params)
```
    Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
    
    Parameters:
    
    params - the parameters for the model
  - fit
```
void fit(org.nd4j.linalg.api.ndarray.INDArray data)
```
    Fit the model to the given data
    
    Parameters:
    
    data - the data to fit the model to
  - iterate
```
void iterate(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Run one iteration
    
    Parameters:
    
    input - the input to iterate on
  - getGradient
```
Gradient getGradient()
```
    Calculate a gradient
    
    Returns:
    
    the gradient for this model
  - gradientAndScore
```
Pair<Gradient,Double> gradientAndScore()
```
    Get the gradient and score
    
    Returns:
    
    the gradient and score
  - batchSize
```
int batchSize()
```
    The current inputs batch size
    
    Returns:
    
    the current inputs batch size
  - conf
```
NeuralNetConfiguration conf()
```
    The configuration for the neural network
    
    Returns:
    
    the configuration for the neural network
  - setConf
```
void setConf(NeuralNetConfiguration conf)
```
    Setter for the configuration
    
    Parameters:
    
    conf -
  - input
```
org.nd4j.linalg.api.ndarray.INDArray input()
```
    The input/feature matrix for the model
    
    Returns:
    
    the input/feature matrix for the model
  - validateInput
```
void validateInput()
```
    Validate the input

Interface Model

Method Summary

Method Detail

fit

update

score

transform

params

numParams

setParams

fit

iterate

getGradient

gradientAndScore

batchSize

conf

setConf

input

validateInput