Uses of Interface
org.deeplearning4j.nn.api.TrainingConfig

Packages that use TrainingConfig

Package	Description
org.deeplearning4j.nn.api
org.deeplearning4j.nn.conf.graph
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.objdetect
org.deeplearning4j.nn.conf.layers.recurrent
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.conf.layers.util
org.deeplearning4j.nn.conf.layers.variational
org.deeplearning4j.nn.conf.layers.wrapper
org.deeplearning4j.nn.conf.misc
org.deeplearning4j.nn.conf.ocnn
org.deeplearning4j.nn.graph.vertex
org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.samediff
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.layers.wrapper
org.deeplearning4j.nn.multilayer

Uses of TrainingConfig in org.deeplearning4j.nn.api

Methods in org.deeplearning4j.nn.api that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	Trainable.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.conf.graph

Classes in org.deeplearning4j.nn.conf.graph that implement TrainingConfig

Modifier and Type	Class and Description
class	AttentionVertex Implements Dot Product Attention using the given inputs.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers

Classes in org.deeplearning4j.nn.conf.layers that implement TrainingConfig

Modifier and Type	Class and Description
class	AbstractLSTM LSTM recurrent net, based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks http://www.cs.toronto.edu/~graves/phd.pdf
class	ActivationLayer Activation layer is a simple layer that applies the specified activation function to the input activations
class	AutoEncoder Autoencoder layer.
class	BaseLayer A neural network layer.
class	BaseOutputLayer
class	BasePretrainNetwork
class	BaseRecurrentLayer
class	BaseUpsamplingLayer Upsampling base layer
class	BatchNormalization Batch normalization layer See: Ioffe and Szegedy, 2014, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift https://arxiv.org/abs/1502.03167
class	CapsuleLayer An implementation of the DigiCaps layer from Dynamic Routing Between Capsules Input should come from a PrimaryCapsules layer and be of shape [mb, inputCaps, inputCapDims].
class	CapsuleStrengthLayer An layer to get the "strength" of each capsule, that is, the probability of it being in the input.
class	CenterLossOutputLayer Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.
class	Cnn3DLossLayer 3D Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: Cnn3DLossLayer does not have any parameters.
class	CnnLossLayer Convolutional Neural Network Loss Layer. Handles calculation of gradients etc for various loss (objective) functions. NOTE: CnnLossLayer does not have any parameters.
class	Convolution1D 1D convolution layer.
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	Convolution2D 2D convolution layer
class	Convolution3D 3D convolution layer configuration
class	ConvolutionLayer 2D Convolution layer (for example, spatial convolution over images).
class	Deconvolution2D 2D deconvolution layer configuration Deconvolutions are also known as transpose convolutions or fractionally strided convolutions.
class	DenseLayer Dense layer: a standard fully connected feed forward layer
class	DepthwiseConvolution2D 2D depth-wise convolution layer configuration.
class	DropoutLayer Dropout layer.
class	EmbeddingLayer Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.
class	EmbeddingSequenceLayer Embedding layer for sequences: feed-forward layer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1.
class	FeedForwardLayer Created by jeffreytang on 7/21/15.
class	GlobalPoolingLayer Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs. Supports the following PoolingTypes: SUM, AVG, MAX, PNORM Global pooling layer can also handle mask arrays when dealing with variable length inputs.
class	GravesBidirectionalLSTM Deprecated. use Bidirectional instead. With the Bidirectional layer wrapper you can make any recurrent layer bidirectional, in particular GravesLSTM. Note that this layer adds the output of both directions, which translates into "ADD" mode in Bidirectional. Usage: .layer(new Bidirectional(Bidirectional.Mode.ADD, new GravesLSTM.Builder()....build()))
class	GravesLSTM Deprecated. Will be eventually removed. Use LSTM instead, which has similar prediction accuracy, but supports CuDNN for faster network training on CUDA (Nvidia) GPUs
class	Layer A neural network layer.
class	LearnedSelfAttentionLayer Implements Dot Product Self Attention with learned queries Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using learned queries.
class	LocallyConnected1D SameDiff version of a 1D locally connected layer.
class	LocallyConnected2D SameDiff version of a 2D locally connected layer.
class	LocalResponseNormalization Local response normalization layer See section 3.3 of http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
class	LossLayer LossLayer is a flexible output layer that performs a loss function on an input without MLP logic. LossLayer is similar to OutputLayer in that both perform loss calculations for network outputs vs.
class	LSTM LSTM recurrent neural network layer without peephole connections.
class	NoParamLayer
class	OutputLayer Output layer used for training via backpropagation based on labels and a specified loss function.
class	Pooling1D 1D Pooling (subsampling) layer.
class	Pooling2D 2D Pooling (subsampling) layer.
class	PReLULayer Parametrized Rectified Linear Unit (PReLU)
class	PrimaryCapsules An implementation of the PrimaryCaps layer from Dynamic Routing Between Capsules Is a reshaped 2D convolution, and the input should be 2D convolutional ([mb, c, h, w]).
class	RecurrentAttentionLayer Implements Recurrent Dot Product Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using the hidden state as the query and all time steps as keys/values.
class	RnnLossLayer Recurrent Neural Network Loss Layer. Handles calculation of gradients etc for various objective (loss) functions. Note: Unlike RnnOutputLayer this RnnLossLayer does not have any parameters - i.e., there is no time distributed dense component here.
class	RnnOutputLayer A version of OutputLayer for recurrent neural networks.
class	SelfAttentionLayer Implements Dot Product Self Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using each timestep as the query.
class	SeparableConvolution2D 2D Separable convolution layer configuration.
class	SpaceToBatchLayer Space to batch utility layer configuration for convolutional input types.
class	SpaceToDepthLayer Space to channels utility layer configuration for convolutional input types.
class	Subsampling1DLayer 1D (temporal) subsampling layer - also known as pooling layer. Expects input of shape [minibatch, nIn, sequenceLength].
class	Subsampling3DLayer 3D subsampling / pooling layer for convolutional neural networks
class	SubsamplingLayer Subsampling layer also referred to as pooling in convolution neural nets Supports the following pooling types: MAX, AVG, SUM, PNORM
class	Upsampling1D Upsampling 1D layer Repeats each step size times along the temporal/sequence axis (dimension 2) For input shape [minibatch, channels, sequenceLength] output has shape [minibatch, channels, size * sequenceLength] Example:
class	Upsampling2D Upsampling 2D layer Repeats each value (or rather, set of depth values) in the height and width dimensions by size[0] and size[1] times respectively. If input has shape [minibatch, channels, height, width] then output has shape [minibatch, channels, height*size[0], width*size[1]] Example:
class	Upsampling3D Upsampling 3D layer Repeats each value (all channel values for each x/y/z location) by size[0], size[1] and size[2] If input has shape [minibatch, channels, depth, height, width] then output has shape [minibatch, channels, size[0] * depth, size[1] * height, size[2] * width]
class	ZeroPadding1DLayer Zero padding 1D layer for convolutional neural networks.
class	ZeroPadding3DLayer Zero padding 3D layer for convolutional neural networks.
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks (2D CNNs).

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.convolutional

Classes in org.deeplearning4j.nn.conf.layers.convolutional that implement TrainingConfig

Modifier and Type	Class and Description
class	Cropping1D Cropping layer for convolutional (1d) neural networks.
class	Cropping2D Cropping layer for convolutional (2d) neural networks.
class	Cropping3D Cropping layer for convolutional (3d) neural networks.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.misc

Classes in org.deeplearning4j.nn.conf.layers.misc that implement TrainingConfig

Modifier and Type	Class and Description
class	ElementWiseMultiplicationLayer Elementwise multiplication layer with weights: implements out = activationFn(input .* w + b) where: - w is a learnable weight vector of length nOut - ".*" is element-wise multiplication - b is a bias vector Note that the input and output sizes of the element-wise layer are the same for this layer
class	FrozenLayer FrozenLayer is used for the purposes of transfer learning. A frozen layer wraps another DL4J Layer within it.
class	FrozenLayerWithBackprop Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
class	RepeatVector RepeatVector layer configuration.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.objdetect

Classes in org.deeplearning4j.nn.conf.layers.objdetect that implement TrainingConfig

Modifier and Type	Class and Description
class	Yolo2OutputLayer Output (loss) layer for YOLOv2 object detection model, based on the papers: YOLO9000: Better, Faster, Stronger - Redmon & Farhadi (2016) - https://arxiv.org/abs/1612.08242 and You Only Look Once: Unified, Real-Time Object Detection - Redmon et al.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.recurrent

Classes in org.deeplearning4j.nn.conf.layers.recurrent that implement TrainingConfig

Modifier and Type	Class and Description
class	Bidirectional Bidirectional is a "wrapper" layer: it wraps any uni-directional RNN layer to make it bidirectional. Note that multiple different modes are supported - these specify how the activations should be combined from the forward and backward RNN networks.
class	LastTimeStep LastTimeStep is a "wrapper" layer: it wraps any RNN (or CNN1D) layer, and extracts out the last time step during forward pass, and returns it as a row vector (per example).
class	SimpleRnn Simple RNN - aka "vanilla" RNN is the simplest type of recurrent neural network layer.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.samediff

Classes in org.deeplearning4j.nn.conf.layers.samediff that implement TrainingConfig

Modifier and Type	Class and Description
class	AbstractSameDiffLayer
class	SameDiffLambdaLayer SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method
class	SameDiffLambdaVertex SameDiffLambdaVertex is defined to be used as the base class for implementing lambda vertices using SameDiff Lambda vertices are vertices without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaVertex and implement a single method to define their vertex
class	SameDiffLayer A base layer used for implementing Deeplearning4j layers using SameDiff.
class	SameDiffOutputLayer A base layer used for implementing Deeplearning4j Output layers using SameDiff.
class	SameDiffVertex A SameDiff-based GraphVertex.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.util

Classes in org.deeplearning4j.nn.conf.layers.util that implement TrainingConfig

Modifier and Type	Class and Description
class	MaskLayer MaskLayer applies the mask array to the forward pass activations, and backward pass gradients, passing through this layer.
class	MaskZeroLayer Wrapper which masks timesteps with activation equal to the specified masking value (0.0 default).

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.variational

Classes in org.deeplearning4j.nn.conf.layers.variational that implement TrainingConfig

Modifier and Type	Class and Description
class	VariationalAutoencoder Variational Autoencoder layer

Uses of TrainingConfig in org.deeplearning4j.nn.conf.layers.wrapper

Classes in org.deeplearning4j.nn.conf.layers.wrapper that implement TrainingConfig

Modifier and Type	Class and Description
class	BaseWrapperLayer Base wrapper layer: the idea is to pass through all methods to the underlying layer, and selectively override them as required.

Uses of TrainingConfig in org.deeplearning4j.nn.conf.misc

Classes in org.deeplearning4j.nn.conf.misc that implement TrainingConfig

Modifier and Type	Class and Description
class	DummyConfig A 'dummy' training configuration for use in frozen layers

Uses of TrainingConfig in org.deeplearning4j.nn.conf.ocnn

Classes in org.deeplearning4j.nn.conf.ocnn that implement TrainingConfig

Modifier and Type	Class and Description
class	OCNNOutputLayer An implementation of one class neural networks from: https://arxiv.org/pdf/1802.06360.pdf The one class neural network approach is an extension of the standard output layer with a single set of weights, an activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional set of weights.

Uses of TrainingConfig in org.deeplearning4j.nn.graph.vertex

Methods in org.deeplearning4j.nn.graph.vertex that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	BaseWrapperVertex.getConfig()
TrainingConfig	BaseGraphVertex.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.graph.vertex.impl

Methods in org.deeplearning4j.nn.graph.vertex.impl that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	LayerVertex.getConfig()
TrainingConfig	FrozenVertex.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.layers

Methods in org.deeplearning4j.nn.layers that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	FrozenLayer.getConfig()
TrainingConfig	AbstractLayer.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.layers.recurrent

Methods in org.deeplearning4j.nn.layers.recurrent that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	BidirectionalLayer.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.layers.samediff

Methods in org.deeplearning4j.nn.layers.samediff that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	SameDiffGraphVertex.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.layers.util

Classes in org.deeplearning4j.nn.layers.util that implement TrainingConfig

Modifier and Type	Class and Description
class	IdentityLayer Identity layer, passes data through unaltered.

Uses of TrainingConfig in org.deeplearning4j.nn.layers.variational

Methods in org.deeplearning4j.nn.layers.variational that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	VariationalAutoencoder.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.layers.wrapper

Methods in org.deeplearning4j.nn.layers.wrapper that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	BaseWrapperLayer.getConfig()

Uses of TrainingConfig in org.deeplearning4j.nn.multilayer

Methods in org.deeplearning4j.nn.multilayer that return TrainingConfig

Modifier and Type	Method and Description
TrainingConfig	MultiLayerNetwork.getConfig()