Uses of Package
org.deeplearning4j.nn.conf

Packages that use org.deeplearning4j.nn.conf

Package	Description
org.deeplearning4j.earlystopping.trainer
org.deeplearning4j.nn.api
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.graph
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.memory
org.deeplearning4j.nn.conf.module
org.deeplearning4j.nn.conf.preprocessor
org.deeplearning4j.nn.conf.serde
org.deeplearning4j.nn.graph
org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.feedforward.rbm
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.params
org.deeplearning4j.nn.transferlearning
org.deeplearning4j.nn.updater
org.deeplearning4j.optimize
org.deeplearning4j.optimize.api
org.deeplearning4j.optimize.solvers
org.deeplearning4j.util

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.earlystopping.trainer

Class and Description
MultiLayerConfiguration Configuration for a multi layer network

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.api

Class and Description
CacheMode
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf

Class and Description
BackpropType Defines the type of backpropagation.
CacheMode
ComputationGraphConfiguration ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
ComputationGraphConfiguration.GraphBuilder
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
GradientNormalization Gradient normalization strategies.
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.
LearningRatePolicy Learning Rate Policy How to decay learning rate during training.
MultiLayerConfiguration Configuration for a multi layer network
MultiLayerConfiguration.Builder
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters
NeuralNetConfiguration.Builder
NeuralNetConfiguration.ListBuilder Fluent interface for building a list of configurations
Updater All the possible different updaters
WorkspaceMode

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.graph

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.layers

Class and Description
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
GradientNormalization Gradient normalization strategies.
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.
LearningRatePolicy Learning Rate Policy How to decay learning rate during training.
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters
Updater All the possible different updaters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.layers.misc

Class and Description
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.
Updater All the possible different updaters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.memory

Class and Description
CacheMode

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.module

Class and Description
ComputationGraphConfiguration.GraphBuilder

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.preprocessor

Class and Description
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.conf.serde

Class and Description
ComputationGraphConfiguration ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
MultiLayerConfiguration Configuration for a multi layer network

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.graph

Class and Description
CacheMode
ComputationGraphConfiguration ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.graph.vertex.impl

Class and Description
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers

Class and Description
CacheMode
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.convolution

Class and Description
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.convolution.subsampling

Class and Description
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.feedforward.autoencoder

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.feedforward.dense

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.feedforward.embedding

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.feedforward.rbm

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.normalization

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.pooling

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.recurrent

Class and Description
CacheMode
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.training

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.layers.variational

Class and Description
CacheMode
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.multilayer

Class and Description
CacheMode
MultiLayerConfiguration Configuration for a multi layer network
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.params

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.transferlearning

Class and Description
BackpropType Defines the type of backpropagation.
ComputationGraphConfiguration ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure.
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
GradientNormalization Gradient normalization strategies.
InputPreProcessor Input pre processor used for pre processing input before passing it to the neural network.
LearningRatePolicy Learning Rate Policy How to decay learning rate during training.
MultiLayerConfiguration Configuration for a multi layer network
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters
NeuralNetConfiguration.Builder
Updater All the possible different updaters
WorkspaceMode

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.nn.updater

Class and Description
LearningRatePolicy Learning Rate Policy How to decay learning rate during training.

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.optimize

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.optimize.api

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.optimize.solvers

Class and Description
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters

Classes in org.deeplearning4j.nn.conf used by org.deeplearning4j.util

Class and Description
ConvolutionMode ConvolutionMode defines how convolution operations should be executed for Convolutional and Subsampling layers, for a given input size and network configuration (specifically stride/padding/kernel sizes). Currently, 3 modes are provided: Strict: Output size for Convolutional and Subsampling layers are calculated as follows, in each dimension: outputSize = (inputSize - kernelSize + 2*padding) / stride + 1.
NeuralNetConfiguration A Serializable configuration for neural nets that covers per layer parameters