TFOpLayer (deeplearning4j 1.0.0-beta7 API)

java.lang.Object
- org.deeplearning4j.nn.conf.layers.Layer
- - org.deeplearning4j.nn.modelimport.keras.layers.TFOpLayer

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig
```
public class TFOpLayer
extends Layer
```
See Also:

Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from class org.deeplearning4j.nn.conf.layers.Layer
  Layer.Builder<T extends Layer.Builder<T>>

Field Summary
- Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer
  constraints, iDropout, layerName

Constructor Summary

Constructors
Constructor and Description

TFOpLayer(Map nodeDef, Map constants)

Constructors
Constructor and Description
`TFOpLayer(Map nodeDef, Map constants)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`GradientNormalization`	`getGradientNormalization()`
`double`	`getGradientNormalizationThreshold()`
`LayerMemoryReport`	`getMemoryReport(InputType inputType)` This is a report of the estimated memory consumption for the given layer
`InputType`	`getOutputType(int idx, InputType inputType)` For a given type of input to this layer, what is the type of the output?
`InputPreProcessor`	`getPreProcessorForInputType(InputType inputType)` For the given type of input to this layer, what preprocessor (if any) is required?
`List<Regularization>`	`getRegularizationByParam(String paramName)` Get the regularization types (l1/l2/weight decay) for the given parameter.
`ParamInitializer`	`initializer()`
`Layer`	`instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)`
`boolean`	`isPretrainParam(String param)` Is the specified parameter a layerwise pretraining only parameter?
`void`	`setNIn(InputType inputType, boolean override)` Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer
clone, getUpdaterByParam, initializeConstraints, resetLayerDefaultConfig, setDataType

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.TrainingConfig
getLayerName

- Constructor Detail
  - TFOpLayer
```
public TFOpLayer(Map nodeDef,
                 Map constants)
```
- Method Detail
  - initializer
```
public ParamInitializer initializer()
```
    Specified by:
    
    initializer in class Layer
    
    Returns:
    
    The parameter initializer for this model
  - getPreProcessorForInputType
```
public InputPreProcessor getPreProcessorForInputType(InputType inputType)
```
    Description copied from class: Layer
    
    For the given type of input to this layer, what preprocessor (if any) is required?
    Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor
    
    Specified by:
    
    getPreProcessorForInputType in class Layer
    
    Parameters:
    
    inputType - InputType to this layer
    
    Returns:
    
    Null if no preprocessor is required, otherwise the type of preprocessor necessary for this layer/input combination
  - isPretrainParam
```
public boolean isPretrainParam(String param)
```
    Description copied from class: Layer
    
    Is the specified parameter a layerwise pretraining only parameter?
    For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
    Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
    
    Specified by:
    
    isPretrainParam in interface TrainingConfig
    
    Specified by:
    
    isPretrainParam in class Layer
    
    Parameters:
    
    param - Parameter name/key
    
    Returns:
    
    True if the parameter is for layerwise pretraining only, false otherwise
  - getOutputType
```
public InputType getOutputType(int idx,
                               InputType inputType)
```
    Description copied from class: Layer
    
    For a given type of input to this layer, what is the type of the output?
    
    Specified by:
    
    getOutputType in class Layer
    
    Parameters:
    
    idx - Index of the layer
    
    inputType - Type of input for the layer
    
    Returns:
    
    Type of output from the layer
  - setNIn
```
public void setNIn(InputType inputType,
                   boolean override)
```
    Description copied from class: Layer
    
    Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type
    
    Specified by:
    
    setNIn in class Layer
    
    Parameters:
    
    inputType - Input type for this layer
    
    override - If false: only set the nIn value if it's not already set. If true: set it regardless of whether it's already set or not.
  - getGradientNormalization
```
public GradientNormalization getGradientNormalization()
```
    Returns:
    
    The gradient normalization configuration
  - instantiate
```
public Layer instantiate(NeuralNetConfiguration conf,
                         Collection<TrainingListener> trainingListeners,
                         int layerIndex,
                         INDArray layerParamsView,
                         boolean initializeParams,
                         DataType networkDataType)
```
    Specified by:
    
    instantiate in class Layer
  - getGradientNormalizationThreshold
```
public double getGradientNormalizationThreshold()
```
    Returns:
    
    The gradient normalization threshold
  - getRegularizationByParam
```
public List<Regularization> getRegularizationByParam(String paramName)
```
    Description copied from class: Layer
    
    Get the regularization types (l1/l2/weight decay) for the given parameter. Different parameters may have different regularization types.
    
    Specified by:
    
    getRegularizationByParam in interface TrainingConfig
    
    Specified by:
    
    getRegularizationByParam in class Layer
    
    Parameters:
    
    paramName - Parameter name ("W", "b" etc)
    
    Returns:
    
    Regularization types (if any) for the specified parameter
  - getMemoryReport
```
public LayerMemoryReport getMemoryReport(InputType inputType)
```
    Description copied from class: Layer
    
    This is a report of the estimated memory consumption for the given layer
    
    Specified by:
    
    getMemoryReport in class Layer
    
    Parameters:
    
    inputType - Input type to the layer. Memory consumption is often a function of the input type
    
    Returns:
    
    Memory report for the layer

Class TFOpLayer

Nested Class Summary

Nested classes/interfaces inherited from class org.deeplearning4j.nn.conf.layers.Layer

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

Constructor Summary

Method Summary

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

Methods inherited from class java.lang.Object

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

Constructor Detail

TFOpLayer

Method Detail

initializer

getPreProcessorForInputType

isPretrainParam

getOutputType

setNIn

getGradientNormalization

instantiate

getGradientNormalizationThreshold

getRegularizationByParam

getMemoryReport