LSTMParamInitializer

java.lang.Object
- org.deeplearning4j.nn.params.LSTMParamInitializer

All Implemented Interfaces:

ParamInitializer
```
public class LSTMParamInitializer
extends java.lang.Object
implements ParamInitializer
```
LSTM Parameter initializer, for LSTM based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf

Field Summary

Fields
Modifier and Type	Field and Description
`static java.lang.String`	`BIAS_KEY`
`static java.lang.String`	`INPUT_WEIGHT_KEY`
`static java.lang.String`	`RECURRENT_WEIGHT_KEY` Weights for previous time step -> current time step connections

Constructor Summary

Constructors
Constructor and Description

LSTMParamInitializer()

Constructors
Constructor and Description
`LSTMParamInitializer()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.util.List<java.lang.String>`	`biasKeys(Layer layer)` Bias parameter keys given the layer configuration
`java.util.Map<java.lang.String,INDArray>`	`getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView)` Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array.
`static LSTMParamInitializer`	`getInstance()`
`java.util.Map<java.lang.String,INDArray>`	`init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams)` Initialize the parameters
`boolean`	`isBiasParam(Layer layer, java.lang.String key)` Is the specified parameter a bias?
`boolean`	`isWeightParam(Layer layer, java.lang.String key)` Is the specified parameter a weight?
`long`	`numParams(Layer l)`
`long`	`numParams(NeuralNetConfiguration conf)`
`java.util.List<java.lang.String>`	`paramKeys(Layer layer)` Get a list of all parameter keys given the layer configuration
`java.util.List<java.lang.String>`	`weightKeys(Layer layer)` Weight parameter keys given the layer configuration

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

- Field Detail
  - RECURRENT_WEIGHT_KEY
```
public static final java.lang.String RECURRENT_WEIGHT_KEY
```
    Weights for previous time step -> current time step connections
    
    See Also:
    
    Constant Field Values
  - BIAS_KEY
```
public static final java.lang.String BIAS_KEY
```
    See Also:
    
    Constant Field Values
  - INPUT_WEIGHT_KEY
```
public static final java.lang.String INPUT_WEIGHT_KEY
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - LSTMParamInitializer
```
public LSTMParamInitializer()
```
- Method Detail
  - getInstance
```
public static LSTMParamInitializer getInstance()
```
  - numParams
```
public long numParams(NeuralNetConfiguration conf)
```
    Specified by:
    
    numParams in interface ParamInitializer
  - numParams
```
public long numParams(Layer l)
```
    Specified by:
    
    numParams in interface ParamInitializer
  - paramKeys
```
public java.util.List<java.lang.String> paramKeys(Layer layer)
```
    Description copied from interface: ParamInitializer
    
    Get a list of all parameter keys given the layer configuration
    
    Specified by:
    
    paramKeys in interface ParamInitializer
    
    Parameters:
    
    layer - Layer
    
    Returns:
    
    All parameter keys
  - weightKeys
```
public java.util.List<java.lang.String> weightKeys(Layer layer)
```
    Description copied from interface: ParamInitializer
    
    Weight parameter keys given the layer configuration
    
    Specified by:
    
    weightKeys in interface ParamInitializer
    
    Parameters:
    
    layer - Layer
    
    Returns:
    
    Weight parameter keys
  - biasKeys
```
public java.util.List<java.lang.String> biasKeys(Layer layer)
```
    Description copied from interface: ParamInitializer
    
    Bias parameter keys given the layer configuration
    
    Specified by:
    
    biasKeys in interface ParamInitializer
    
    Parameters:
    
    layer - Layer
    
    Returns:
    
    Bias parameter keys
  - isWeightParam
```
public boolean isWeightParam(Layer layer,
                             java.lang.String key)
```
    Description copied from interface: ParamInitializer
    
    Is the specified parameter a weight?
    
    Specified by:
    
    isWeightParam in interface ParamInitializer
    
    Parameters:
    
    layer - Layer
    
    key - Key to check
    
    Returns:
    
    True if parameter is a weight
  - isBiasParam
```
public boolean isBiasParam(Layer layer,
                           java.lang.String key)
```
    Description copied from interface: ParamInitializer
    
    Is the specified parameter a bias?
    
    Specified by:
    
    isBiasParam in interface ParamInitializer
    
    Parameters:
    
    layer - Layer
    
    key - Key to check
    
    Returns:
    
    True if parameter is a bias
  - init
```
public java.util.Map<java.lang.String,INDArray> init(NeuralNetConfiguration conf,
                                                     INDArray paramsView,
                                                     boolean initializeParams)
```
    Description copied from interface: ParamInitializer
    
    Initialize the parameters
    
    Specified by:
    
    init in interface ParamInitializer
    
    Parameters:
    
    conf - the configuration
    
    paramsView - a view of the full network (backprop) parameters
    
    initializeParams - if true: initialize the parameters according to the configuration. If false: don't modify the values in the paramsView array (but do select out the appropriate subset, reshape etc as required)
    
    Returns:
    
    Map of parameters keyed by type (view of the 'paramsView' array)
  - getGradientsFromFlattened
```
public java.util.Map<java.lang.String,INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf,
                                                                          INDArray gradientView)
```
    Description copied from interface: ParamInitializer
    
    Return a map of gradients (in their standard non-flattened representation), taken from the flattened (row vector) gradientView array. The idea is that operates in exactly the same way as the the paramsView does in #init(Map, NeuralNetConfiguration, INDArray); thus the position in the view (and, the array orders) must match those of the parameters
    
    Specified by:
    
    getGradientsFromFlattened in interface ParamInitializer
    
    Parameters:
    
    conf - Configuration
    
    gradientView - The flattened gradients array, as a view of the larger array
    
    Returns:
    
    A map containing an array by parameter type, that is a view of the full network gradients array

Class LSTMParamInitializer

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

RECURRENT_WEIGHT_KEY

BIAS_KEY

INPUT_WEIGHT_KEY

Constructor Detail

LSTMParamInitializer

Method Detail

getInstance

numParams

numParams

paramKeys

weightKeys

biasKeys

isWeightParam

isBiasParam

init

getGradientsFromFlattened