BaseLayer (deeplearning4j-core 0.0.3.3 API)

java.lang.Object
- org.deeplearning4j.nn.layers.BaseLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

BasePretrainNetwork, ConvolutionDownSampleLayer, LSTM, OutputLayer, RecursiveAutoEncoder
```
public abstract class BaseLayer
extends Object
implements Layer
```
A layer with a bias and activation function

Author:

Adam Gibson

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected NeuralNetConfiguration`	`conf`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`dropoutMask`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`input`
`protected ParamInitializer`	`paramInitializer`
`protected Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`params`

Constructor Summary

Constructors
Constructor and Description

BaseLayer(NeuralNetConfiguration conf)

BaseLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Constructors
Constructor and Description
`BaseLayer(NeuralNetConfiguration conf)`
`BaseLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`org.nd4j.linalg.api.ndarray.INDArray`	`activate()` Trigger an activation with the last specified input
`org.nd4j.linalg.api.ndarray.INDArray`	`activate(org.nd4j.linalg.api.ndarray.INDArray input)` Initialize the layer with the given input and return the activation for this layer given this input
`org.nd4j.linalg.api.ndarray.INDArray`	`activationMean()`
`protected void`	`applyDropOutIfNecessary(org.nd4j.linalg.api.ndarray.INDArray input)`
`int`	`batchSize()` The current inputs batch size
`Layer`	`clone()`
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`protected Gradient`	`createGradient(org.nd4j.linalg.api.ndarray.INDArray... gradients)` Create a gradient list based on the passed in parameters.
`void`	`fit()` All models have a fit method
`void`	`fit(org.nd4j.linalg.api.ndarray.INDArray input)` Fit the model to the given data
`org.nd4j.linalg.api.ndarray.INDArray`	`getInput()`
`org.nd4j.linalg.api.ndarray.INDArray`	`getParam(String param)` Get the parameter
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`void`	`initParams()` Initialize the parameters
`org.nd4j.linalg.api.ndarray.INDArray`	`input()` The input/feature matrix for the model
`void`	`merge(Layer l, int batchSize)` Averages the given logistic regression from a mini batch in to this one
`int`	`numParams()` The number of parameters for the model
`org.nd4j.linalg.api.ndarray.INDArray`	`params()` Returns the parameters of the neural network
`Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`paramTable()` The param table
`org.nd4j.linalg.api.ndarray.INDArray`	`preOutput(org.nd4j.linalg.api.ndarray.INDArray x)` Classify input
`void`	`setConf(NeuralNetConfiguration conf)` Setter for the configuration
`void`	`setConfiguration(NeuralNetConfiguration conf)`
`void`	`setInput(org.nd4j.linalg.api.ndarray.INDArray input)`
`void`	`setParam(String key, org.nd4j.linalg.api.ndarray.INDArray val)` Set the parameter with a new ndarray
`void`	`setParams(org.nd4j.linalg.api.ndarray.INDArray params)` Set the parameters for this model.
`void`	`setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable)`
`Layer`	`transpose()` Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
`void`	`validateInput()` Validate the input

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.deeplearning4j.nn.api.Model
getGradient, iterate, score, transform, update

- Field Detail
  - input
```
protected org.nd4j.linalg.api.ndarray.INDArray input
```
  - params
```
protected Map<String,org.nd4j.linalg.api.ndarray.INDArray> params
```
  - conf
```
protected NeuralNetConfiguration conf
```
  - dropoutMask
```
protected org.nd4j.linalg.api.ndarray.INDArray dropoutMask
```
  - paramInitializer
```
protected ParamInitializer paramInitializer
```
- Constructor Detail
  - BaseLayer
```
public BaseLayer(NeuralNetConfiguration conf)
```
  - BaseLayer
```
public BaseLayer(NeuralNetConfiguration conf,
                 org.nd4j.linalg.api.ndarray.INDArray input)
```
- Method Detail
  - fit
```
public void fit()
```
    Description copied from interface: Model
    
    All models have a fit method
    
    Specified by:
    
    fit in interface Model
  - setConf
```
public void setConf(NeuralNetConfiguration conf)
```
    Description copied from interface: Model
    
    Setter for the configuration
    
    Specified by:
    
    setConf in interface Model
  - setParam
```
public void setParam(String key,
                     org.nd4j.linalg.api.ndarray.INDArray val)
```
    Description copied from interface: Layer
    
    Set the parameter with a new ndarray
    
    Specified by:
    
    setParam in interface Layer
    
    Parameters:
    
    key - the key to se t
    
    val - the new ndarray
  - params
```
public org.nd4j.linalg.api.ndarray.INDArray params()
```
    Returns the parameters of the neural network
    
    Specified by:
    
    params in interface Model
    
    Returns:
    
    the parameters of the neural network
  - setParams
```
public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)
```
    Description copied from interface: Model
    
    Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
    
    Specified by:
    
    setParams in interface Model
    
    Parameters:
    
    params - the parameters for the model
  - initParams
```
public void initParams()
```
    Description copied from interface: Layer
    
    Initialize the parameters
    
    Specified by:
    
    initParams in interface Layer
  - paramTable
```
public Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable()
```
    Description copied from interface: Layer
    
    The param table
    
    Specified by:
    
    paramTable in interface Layer
    
    Returns:
  - setParamTable
```
public void setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable)
```
    Specified by:
    
    setParamTable in interface Layer
  - getParam
```
public org.nd4j.linalg.api.ndarray.INDArray getParam(String param)
```
    Description copied from interface: Layer
    
    Get the parameter
    
    Specified by:
    
    getParam in interface Layer
    
    Parameters:
    
    param - the key of the parameter
    
    Returns:
    
    the parameter vector/matrix with that particular key
  - preOutput
```
public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x)
```
    Classify input
    
    Specified by:
    
    preOutput in interface Layer
    
    Parameters:
    
    x - the input (can either be a matrix or vector) If it's a matrix, each row is considered an example and associated rows are classified accordingly. Each row will be the likelihood of a label given that example
    
    Returns:
    
    a probability distribution for each row
  - batchSize
```
public int batchSize()
```
    Description copied from interface: Model
    
    The current inputs batch size
    
    Specified by:
    
    batchSize in interface Model
    
    Returns:
    
    the current inputs batch size
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate()
```
    Description copied from interface: Layer
    
    Trigger an activation with the last specified input
    
    Specified by:
    
    activate in interface Layer
    
    Returns:
    
    the activation of the last specified input
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Description copied from interface: Layer
    
    Initialize the layer with the given input and return the activation for this layer given this input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    input - the input to use
    
    Returns:
  - activationMean
```
public org.nd4j.linalg.api.ndarray.INDArray activationMean()
```
    Specified by:
    
    activationMean in interface Layer
  - conf
```
public NeuralNetConfiguration conf()
```
    Description copied from interface: Model
    
    The configuration for the neural network
    
    Specified by:
    
    conf in interface Layer
    
    Specified by:
    
    conf in interface Model
    
    Returns:
    
    the configuration for the neural network
  - setConfiguration
```
public void setConfiguration(NeuralNetConfiguration conf)
```
    Specified by:
    
    setConfiguration in interface Layer
  - getInput
```
public org.nd4j.linalg.api.ndarray.INDArray getInput()
```
    Specified by:
    
    getInput in interface Layer
  - setInput
```
public void setInput(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Specified by:
    
    setInput in interface Layer
  - applyDropOutIfNecessary
```
protected void applyDropOutIfNecessary(org.nd4j.linalg.api.ndarray.INDArray input)
```
  - merge
```
public void merge(Layer l,
                  int batchSize)
```
    Averages the given logistic regression from a mini batch in to this one
    
    Specified by:
    
    merge in interface Layer
    
    Parameters:
    
    l - the logistic regression to average in to this one
    
    batchSize - the batch size
  - clone
```
public Layer clone()
```
    Specified by:
    
    clone in interface Layer
    
    Overrides:
    
    clone in class Object
  - numParams
```
public int numParams()
```
    The number of parameters for the model
    
    Specified by:
    
    numParams in interface Model
    
    Returns:
    
    the number of parameters for the model
  - fit
```
public void fit(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Description copied from interface: Model
    
    Fit the model to the given data
    
    Specified by:
    
    fit in interface Model
    
    Parameters:
    
    input - the data to fit the model to
  - gradientAndScore
```
public Pair<Gradient,Double> gradientAndScore()
```
    Description copied from interface: Model
    
    Get the gradient and score
    
    Specified by:
    
    gradientAndScore in interface Model
    
    Returns:
    
    the gradient and score
  - input
```
public org.nd4j.linalg.api.ndarray.INDArray input()
```
    Description copied from interface: Model
    
    The input/feature matrix for the model
    
    Specified by:
    
    input in interface Model
    
    Returns:
    
    the input/feature matrix for the model
  - validateInput
```
public void validateInput()
```
    Description copied from interface: Model
    
    Validate the input
    
    Specified by:
    
    validateInput in interface Model
  - createGradient
```
protected Gradient createGradient(org.nd4j.linalg.api.ndarray.INDArray... gradients)
```
    Create a gradient list based on the passed in parameters. Will throw an IllegalArgumentException if the number of gradient matrices isn't equal to the number of keys in the parameter list
    
    Parameters:
    
    gradients - the gradients to create from
    
    Returns:
    
    the create based on the passed in ndarrays
  - transpose
```
public Layer transpose()
```
    Description copied from interface: Layer
    
    Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
    
    Specified by:
    
    transpose in interface Layer
    
    Returns:
    
    the transposed layer

Class BaseLayer

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface org.deeplearning4j.nn.api.Model

Field Detail

input

params

conf

dropoutMask

paramInitializer

Constructor Detail

BaseLayer

BaseLayer

Method Detail

fit

setConf

setParam

params

setParams

initParams

paramTable

setParamTable

getParam

preOutput

batchSize

activate

activate

activationMean

conf

setConfiguration

getInput

setInput

applyDropOutIfNecessary

merge

clone

numParams

fit

gradientAndScore

input

validateInput

createGradient

transpose