java.lang.Object
- org.deeplearning4j.nn.conf.layers.Layer.Builder<T>
- - org.deeplearning4j.nn.conf.layers.BaseLayer.Builder<T>
  - - org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder<T>
    - - org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BaseConvBuilder<T>

Direct Known Subclasses:

Convolution1DLayer.Builder, Convolution3D.Builder, ConvolutionLayer.Builder, Deconvolution2D.Builder, Deconvolution3D.Builder, DepthwiseConvolution2D.Builder, SeparableConvolution2D.Builder

Enclosing class:

ConvolutionLayer
```
public abstract static class ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>
extends FeedForwardLayer.Builder<T>
```

Field Summary

Fields
Modifier and Type	Field	Description
`protected int`	`convolutionDim`
`protected ConvolutionMode`	`convolutionMode`	Set the convolution mode for the Convolution layer.
`protected ConvolutionLayer.AlgoMode`	`cudnnAlgoMode`	Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
`protected boolean`	`cudnnAllowFallback`	When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user.
`protected ConvolutionLayer.BwdDataAlgo`	`cudnnBwdDataAlgo`
`protected ConvolutionLayer.BwdFilterAlgo`	`cudnnBwdFilterAlgo`
`protected ConvolutionLayer.FwdAlgo`	`cudnnFwdAlgo`
`protected int[]`	`dilation`	Kernel dilation.
`protected boolean`	`hasBias`	If true (default): include bias parameters in the model.
`int[]`	`kernelSize`
`protected int[]`	`padding`
`protected int[]`	`stride`

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn, nOut

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iupdater, regularization, regularizationBias, weightInitFn, weightNoise

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
allParamConstraints, biasConstraints, iDropout, layerName, weightConstraints

Constructor Summary

Constructors
Modifier	Constructor	Description
`protected`	`BaseConvBuilder()`
`protected`	`BaseConvBuilder(int... kernelSize)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride, int dim)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int dim)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int[] dilation)`
`protected`	`BaseConvBuilder(int[] kernelSize, int[] stride, int[] padding, int[] dilation, int dim)`
`protected`	`BaseConvBuilder(int dim, int... kernelSize)`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods Deprecated Methods
Modifier and Type	Method	Description
`protected abstract boolean`	`allowCausal()`
`T`	`convolutionMode(ConvolutionMode convolutionMode)`	Set the convolution mode for the Convolution layer.
`T`	`cudnnAlgoMode(ConvolutionLayer.AlgoMode cudnnAlgoMode)`	Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
`T`	`cudnnAllowFallback(boolean allowFallback)`	Deprecated. Use `helperAllowFallback(boolean)`
`T`	`cudnnBwdDataMode(ConvolutionLayer.BwdDataAlgo cudnnBwdDataAlgo)`
`T`	`cudnnBwdFilterMode(ConvolutionLayer.BwdFilterAlgo cudnnBwdFilterAlgo)`
`T`	`cudnnFwdMode(ConvolutionLayer.FwdAlgo cudnnFwdAlgo)`
`T`	`dilation(int... dilation)`	Kernel dilation.
`T`	`hasBias(boolean hasBias)`	If true (default): include bias parameters in the model.
`T`	`helperAllowFallback(boolean allowFallback)`	When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user.
`T`	`kernelSize(int... kernelSize)`
`T`	`padding(int... padding)`
`protected void`	`setConvolutionMode(ConvolutionMode convolutionMode)`
`T`	`stride(int... stride)`

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn, nIn, nOut, nOut, units

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activation, activation, biasInit, biasUpdater, dist, gainInit, gradientNormalization, gradientNormalizationThreshold, l1, l1Bias, l2, l2Bias, regularization, regularizationBias, updater, updater, weightDecay, weightDecay, weightDecayBias, weightDecayBias, weightInit, weightInit, weightInit, weightNoise

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
build, constrainAllParameters, constrainBias, constrainWeights, dropOut, dropOut, name

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

- Field Detail
  - convolutionDim
```
protected int convolutionDim
```
  - hasBias
```
protected boolean hasBias
```
    If true (default): include bias parameters in the model. False: no bias.
  - convolutionMode
```
protected ConvolutionMode convolutionMode
```
    Set the convolution mode for the Convolution layer. See ConvolutionMode for more details
  - dilation
```
protected int[] dilation
```
    Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions, which are also known as atrous convolutions.
    For more details, see: Yu and Koltun (2014) and Chen et al. (2014), as well as http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions
  - kernelSize
```
public int[] kernelSize
```
  - stride
```
protected int[] stride
```
  - padding
```
protected int[] padding
```
  - cudnnAlgoMode
```
protected ConvolutionLayer.AlgoMode cudnnAlgoMode
```
    Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
  - cudnnFwdAlgo
```
protected ConvolutionLayer.FwdAlgo cudnnFwdAlgo
```
  - cudnnBwdFilterAlgo
```
protected ConvolutionLayer.BwdFilterAlgo cudnnBwdFilterAlgo
```
  - cudnnBwdDataAlgo
```
protected ConvolutionLayer.BwdDataAlgo cudnnBwdDataAlgo
```
  - cudnnAllowFallback
```
protected boolean cudnnAllowFallback
```
    When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user. If false, the built-in (non-CuDNN) implementation for ConvolutionLayer will be used
- Constructor Detail
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride,
                          int[] padding,
                          int[] dilation,
                          int dim)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride,
                          int[] padding,
                          int[] dilation)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride,
                          int[] padding,
                          int dim)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride,
                          int[] padding)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride,
                          int dim)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int[] kernelSize,
                          int[] stride)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int dim,
                          int... kernelSize)
```
  - BaseConvBuilder
```
protected BaseConvBuilder(int... kernelSize)
```
  - BaseConvBuilder
```
protected BaseConvBuilder()
```
- Method Detail
  - allowCausal
```
protected abstract boolean allowCausal()
```
  - setConvolutionMode
```
protected void setConvolutionMode(ConvolutionMode convolutionMode)
```
  - hasBias
```
public T hasBias(boolean hasBias)
```
    If true (default): include bias parameters in the model. False: no bias.
    
    Parameters:
    
    hasBias - If true: include bias parameters in this model
  - convolutionMode
```
public T convolutionMode(ConvolutionMode convolutionMode)
```
    Set the convolution mode for the Convolution layer. See ConvolutionMode for more details
    
    Parameters:
    
    convolutionMode - Convolution mode for layer
  - dilation
```
public T dilation(int... dilation)
```
    Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions, which are also known as atrous convolutions.
    For more details, see: Yu and Koltun (2014) and Chen et al. (2014), as well as http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html#dilated-convolutions
    
    Parameters:
    
    dilation - Dilation for kernel
  - kernelSize
```
public T kernelSize(int... kernelSize)
```
  - stride
```
public T stride(int... stride)
```
  - padding
```
public T padding(int... padding)
```
  - cudnnAlgoMode
```
public T cudnnAlgoMode(ConvolutionLayer.AlgoMode cudnnAlgoMode)
```
    Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
  - cudnnFwdMode
```
public T cudnnFwdMode(ConvolutionLayer.FwdAlgo cudnnFwdAlgo)
```
  - cudnnBwdFilterMode
```
public T cudnnBwdFilterMode(ConvolutionLayer.BwdFilterAlgo cudnnBwdFilterAlgo)
```
  - cudnnBwdDataMode
```
public T cudnnBwdDataMode(ConvolutionLayer.BwdDataAlgo cudnnBwdDataAlgo)
```
  - cudnnAllowFallback
```
@Deprecated
public T cudnnAllowFallback(boolean allowFallback)
```
    Deprecated.
    Use helperAllowFallback(boolean)
    
    When using CuDNN and an error is encountered, should fallback to the non-CuDNN implementatation be allowed? If set to false, an exception in CuDNN will be propagated back to the user. If true, the built-in (non-CuDNN) implementation for ConvolutionLayer will be used
    
    Parameters:
    
    allowFallback - Whether fallback to non-CuDNN implementation should be used
  - helperAllowFallback
```
public T helperAllowFallback(boolean allowFallback)
```
    When using CuDNN or MKLDNN and an error is encountered, should fallback to the non-helper implementation be allowed? If set to false, an exception in the helper will be propagated back to the user. If true, the built-in (non-MKL/CuDNN) implementation for ConvolutionLayer will be used
    
    Parameters:
    
    allowFallback - Whether fallback to non-CuDNN implementation should be used

Class ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

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

Constructor Summary

Method Summary

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder

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

Methods inherited from class java.lang.Object

Field Detail

convolutionDim

hasBias

convolutionMode

dilation

kernelSize

stride

padding

cudnnAlgoMode

cudnnFwdAlgo

cudnnBwdFilterAlgo

cudnnBwdDataAlgo

cudnnAllowFallback

Constructor Detail

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

BaseConvBuilder

Method Detail

allowCausal

setConvolutionMode

hasBias

convolutionMode

dilation

kernelSize

stride

padding

cudnnAlgoMode

cudnnFwdMode

cudnnBwdFilterMode

cudnnBwdDataMode

cudnnAllowFallback

helperAllowFallback