mlpack::ann Namespace Reference

Artificial Neural Network. More...

Classes
class	AdaptiveMaxPooling
	Implementation of the AdaptiveMaxPooling layer. More...
class	AdaptiveMeanPooling
	Implementation of the AdaptiveMeanPooling. More...
class	Add
	Implementation of the Add module class. More...
class	AddMerge
	Implementation of the AddMerge module class. More...
class	AddVisitor
	AddVisitor exposes the Add() method of the given module. More...
class	AlphaDropout
	The alpha - dropout layer is a regularizer that randomly with probability 'ratio' sets input values to alphaDash. More...
class	AtrousConvolution
	Implementation of the Atrous Convolution class. More...
class	BackwardVisitor
	BackwardVisitor executes the Backward() function given the input, error and delta parameter. More...
class	BaseLayer
	Implementation of the base layer. More...
class	BatchNorm
	Declaration of the Batch Normalization layer class. More...
class	BCELoss
	The binary-cross-entropy performance function measures the Binary Cross Entropy between the target and the output. More...
class	BernoulliDistribution
	Multiple independent Bernoulli distributions. More...
class	BiasSetVisitor
	BiasSetVisitor updates the module bias parameters given the parameters set. More...
class	BilinearInterpolation
	Definition and Implementation of the Bilinear Interpolation Layer. More...
class	BinaryRBM
	For more information, see the following paper: More...
class	BRNN
	Implementation of a standard bidirectional recurrent neural network container. More...
class	CELU
	The CELU activation function, defined by. More...
class	ChannelShuffle
	Definition and implementation of the Channel Shuffle Layer. More...
class	Concat
	Implementation of the Concat class. More...
class	Concatenate
	Implementation of the Concatenate module class. More...
class	ConcatPerformance
	Implementation of the concat performance class. More...
class	Constant
	Implementation of the constant layer. More...
class	ConstInitialization
	This class is used to initialize weight matrix with constant values. More...
class	Convolution
	Implementation of the Convolution class. More...
class	CopyVisitor
	This visitor is to support copy constructor for neural network module. More...
class	CosineEmbeddingLoss
	Cosine Embedding Loss function is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning. More...
class	CReLU
	A concatenated ReLU has two outputs, one ReLU and one negative ReLU, concatenated together. More...
class	DCGAN
	For more information, see the following paper: More...
class	DeleteVisitor
	DeleteVisitor executes the destructor of the instantiated object. More...
class	DeltaVisitor
	DeltaVisitor exposes the delta parameter of the given module. More...
class	DeterministicSetVisitor
	DeterministicSetVisitor set the deterministic parameter given the deterministic value. More...
class	DiceLoss
	The dice loss performance function measures the network's performance according to the dice coefficient between the input and target distributions. More...
class	DropConnect
	The DropConnect layer is a regularizer that randomly with probability ratio sets the connection values to zero and scales the remaining elements by factor 1 /(1 - ratio). More...
class	Dropout
	The dropout layer is a regularizer that randomly with probability 'ratio' sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio) rather than during test time so as to keep the expected sum same. More...
class	EarthMoverDistance
	The earth mover distance function measures the network's performance according to the Kantorovich-Rubinstein duality approximation. More...
class	ElishFunction
	The ELiSH function, defined by. More...
class	ElliotFunction
	The Elliot function, defined by. More...
class	ELU
	The ELU activation function, defined by. More...
class	EmptyLoss
	The empty loss does nothing, letting the user calculate the loss outside the model. More...
class	FastLSTM
	An implementation of a faster version of the Fast LSTM network layer. More...
class	FFN
	Implementation of a standard feed forward network. More...
class	FFTConvolution
	Computes the two-dimensional convolution through fft. More...
class	FlattenTSwish
	The Flatten T Swish activation function, defined by. More...
class	FlexibleReLU
	The FlexibleReLU activation function, defined by. More...
class	ForwardVisitor
	ForwardVisitor executes the Forward() function given the input and output parameter. More...
class	FullConvolution
class	GAN
	The implementation of the standard GAN module. More...
class	GaussianFunction
	The gaussian function, defined by. More...
class	GaussianInitialization
	This class is used to initialize weigth matrix with a gaussian. More...
class	GELUFunction
	The GELU function, defined by. More...
class	Glimpse
	The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing scale around a given location in a given image. More...
class	GlorotInitializationType
	This class is used to initialize the weight matrix with the Glorot Initialization method. More...
class	GradientSetVisitor
	GradientSetVisitor update the gradient parameter given the gradient set. More...
class	GradientUpdateVisitor
	GradientUpdateVisitor update the gradient parameter given the gradient set. More...
class	GradientVisitor
	SearchModeVisitor executes the Gradient() method of the given module using the input and delta parameter. More...
class	GradientZeroVisitor
class	GRU
	An implementation of a gru network layer. More...
class	HardShrink
	Hard Shrink operator is defined as,. More...
class	HardSigmoidFunction
	The hard sigmoid function, defined by. More...
class	HardSwishFunction
	The Hard Swish function, defined by. More...
class	HardTanH
	The Hard Tanh activation function, defined by. More...
class	HeInitialization
	This class is used to initialize weight matrix with the He initialization rule given by He et. More...
class	Highway
	Implementation of the Highway layer. More...
class	HingeEmbeddingLoss
	The Hinge Embedding loss function is often used to compute the loss between y_true and y_pred. More...
class	HingeLoss
	Computes the hinge loss between \(y_true\) and \(y_pred\). More...
class	HuberLoss
	The Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. More...
class	IdentityFunction
	The identity function, defined by. More...
class	InitTraits
	This is a template class that can provide information about various initialization methods. More...
class	InitTraits< KathirvalavakumarSubavathiInitialization >
	Initialization traits of the kathirvalavakumar subavath initialization rule. More...
class	InitTraits< NguyenWidrowInitialization >
	Initialization traits of the Nguyen-Widrow initialization rule. More...
class	InShapeVisitor
	InShapeVisitor returns the input shape a Layer expects. More...
class	InvQuadFunction
	The Inverse Quadratic function, defined by. More...
class	ISRLU
	The ISRLU activation function, defined by. More...
class	Join
	Implementation of the Join module class. More...
class	KathirvalavakumarSubavathiInitialization
	This class is used to initialize the weight matrix with the method proposed by T. More...
class	KLDivergence
	The Kullback–Leibler divergence is often used for continuous distributions (direct regression). More...
class	L1Loss
	The L1 loss is a loss function that measures the mean absolute error (MAE) between each element in the input x and target y. More...
class	LayerNorm
	Declaration of the Layer Normalization class. More...
class	LayerTraits
	This is a template class that can provide information about various layers. More...
class	LeakyReLU
	The LeakyReLU activation function, defined by. More...
class	LecunNormalInitialization
	This class is used to initialize weight matrix with the Lecun Normalization initialization rule. More...
class	Linear
	Implementation of the Linear layer class. More...
class	Linear3D
	Implementation of the Linear3D layer class. More...
class	LinearNoBias
	Implementation of the LinearNoBias class. More...
class	LiSHTFunction
	The LiSHT function, defined by. More...
class	LoadOutputParameterVisitor
	LoadOutputParameterVisitor restores the output parameter using the given parameter set. More...
class	LogCoshLoss
	The Log-Hyperbolic-Cosine loss function is often used to improve variational auto encoder. More...
class	LogisticFunction
	The logistic function, defined by. More...
class	LogSoftMax
	Implementation of the log softmax layer. More...
class	Lookup
	The Lookup class stores word embeddings and retrieves them using tokens. More...
class	LossVisitor
	LossVisitor exposes the Loss() method of the given module. More...
class	LpPooling
	Implementation of the LPPooling. More...
class	LRegularizer
	The L_p regularizer for arbitrary integer p. More...
class	LSTM
	Implementation of the LSTM module class. More...
class	MarginRankingLoss
	Margin ranking loss measures the loss given inputs and a label vector with values of 1 or -1. More...
class	MaxPooling
	Implementation of the MaxPooling layer. More...
class	MaxPoolingRule
class	MeanAbsolutePercentageError
	The mean absolute percentage error performance function measures the network's performance according to the mean of the absolute difference between input and target divided by target. More...
class	MeanBiasError
	The mean bias error performance function measures the network's performance according to the mean of errors. More...
class	MeanPooling
	Implementation of the MeanPooling. More...
class	MeanPoolingRule
class	MeanSquaredError
	The mean squared error performance function measures the network's performance according to the mean of squared errors. More...
class	MeanSquaredLogarithmicError
	The mean squared logarithmic error performance function measures the network's performance according to the mean of squared logarithmic errors. More...
class	MiniBatchDiscrimination
	Implementation of the MiniBatchDiscrimination layer. More...
class	MishFunction
	The Mish function, defined by. More...
class	MultiheadAttention
	Multihead Attention allows the model to jointly attend to information from different representation subspaces at different positions. More...
class	MultiplyConstant
	Implementation of the multiply constant layer. More...
class	MultiplyMerge
	Implementation of the MultiplyMerge module class. More...
class	MultiQuadFunction
	The Multi Quadratic function, defined by. More...
class	NaiveConvolution
	Computes the two-dimensional convolution. More...
class	NearestInterpolation
	Definition and Implementation of the Nearest Interpolation Layer. More...
class	NegativeLogLikelihood
	Implementation of the negative log likelihood layer. More...
class	NetworkInitialization
	This class is used to initialize the network with the given initialization rule. More...
class	NguyenWidrowInitialization
	This class is used to initialize the weight matrix with the Nguyen-Widrow method. More...
class	NoisyLinear
	Implementation of the NoisyLinear layer class. More...
class	NoRegularizer
	Implementation of the NoRegularizer. More...
class	NormalDistribution
	Implementation of the Normal Distribution function. More...
class	OivsInitialization
	This class is used to initialize the weight matrix with the oivs method. More...
class	OrthogonalInitialization
	This class is used to initialize the weight matrix with the orthogonal matrix initialization. More...
class	OrthogonalRegularizer
	Implementation of the OrthogonalRegularizer. More...
class	OutputHeightVisitor
	OutputHeightVisitor exposes the OutputHeight() method of the given module. More...
class	OutputParameterVisitor
	OutputParameterVisitor exposes the output parameter of the given module. More...
class	OutputWidthVisitor
	OutputWidthVisitor exposes the OutputWidth() method of the given module. More...
class	Padding
	Implementation of the Padding module class. More...
class	ParametersSetVisitor
	ParametersSetVisitor update the parameters set using the given matrix. More...
class	ParametersVisitor
	ParametersVisitor exposes the parameters set of the given module and stores the parameters set into the given matrix. More...
class	PixelShuffle
	Implementation of the PixelShuffle layer. More...
class	Poisson1Function
	The Poisson one function, defined by. More...
class	PoissonNLLLoss
	Implementation of the Poisson negative log likelihood loss. More...
class	PositionalEncoding
	Positional Encoding injects some information about the relative or absolute position of the tokens in the sequence. More...
class	PReLU
	The PReLU activation function, defined by (where alpha is trainable) More...
class	QuadraticFunction
	The Quadratic function, defined by. More...
class	RandomInitialization
	This class is used to initialize randomly the weight matrix. More...
class	RBF
	Implementation of the Radial Basis Function layer. More...
class	RBM
	The implementation of the RBM module. More...
class	ReconstructionLoss
	The reconstruction loss performance function measures the network's performance equal to the negative log probability of the target with the input distribution. More...
class	RectifierFunction
	The rectifier function, defined by. More...
class	Recurrent
	Implementation of the RecurrentLayer class. More...
class	RecurrentAttention
	This class implements the Recurrent Model for Visual Attention, using a variety of possible layer implementations. More...
class	ReinforceNormal
	Implementation of the reinforce normal layer. More...
class	ReLU6
class	Reparametrization
	Implementation of the Reparametrization layer class. More...
class	ResetCellVisitor
	ResetCellVisitor executes the ResetCell() function. More...
class	ResetVisitor
	ResetVisitor executes the Reset() function. More...
class	RewardSetVisitor
	RewardSetVisitor set the reward parameter given the reward value. More...
class	RNN
	Implementation of a standard recurrent neural network container. More...
class	RunSetVisitor
	RunSetVisitor set the run parameter given the run value. More...
class	SaveOutputParameterVisitor
	SaveOutputParameterVisitor saves the output parameter into the given parameter set. More...
class	Select
	The select module selects the specified column from a given input matrix. More...
class	Sequential
	Implementation of the Sequential class. More...
class	SetInputHeightVisitor
	SetInputHeightVisitor updates the input height parameter with the given input height. More...
class	SetInputWidthVisitor
	SetInputWidthVisitor updates the input width parameter with the given input width. More...
class	SigmoidCrossEntropyError
	The SigmoidCrossEntropyError performance function measures the network's performance according to the cross-entropy function between the input and target distributions. More...
class	SILUFunction
	The SILU function, defined by. More...
class	SoftMarginLoss
class	Softmax
	Implementation of the Softmax layer. More...
class	Softmin
	Implementation of the Softmin layer. More...
class	SoftplusFunction
	The softplus function, defined by. More...
class	SoftShrink
	Soft Shrink operator is defined as, \begin{eqnarray*} f(x) &=& \begin{cases} x - \lambda & : x > \lambda \\ x + \lambda & : x < -\lambda \\ 0 & : otherwise. \\ \end{cases} \\ f'(x) &=& \begin{cases} 1 & : x > \lambda \\ 1 & : x < -\lambda \\ 0 & : otherwise. \end{cases} \end{eqnarray*} . More...
class	SoftsignFunction
	The softsign function, defined by. More...
class	SpatialDropout
	Implementation of the SpatialDropout layer. More...
class	SpikeSlabRBM
	For more information, see the following paper: More...
class	SplineFunction
	The Spline function, defined by. More...
class	StandardGAN
	For more information, see the following paper: More...
class	Subview
	Implementation of the subview layer. More...
class	SVDConvolution
	Computes the two-dimensional convolution using singular value decomposition. More...
class	SwishFunction
	The swish function, defined by. More...
class	TanhExpFunction
	The TanhExp function, defined by. More...
class	TanhFunction
	The tanh function, defined by. More...
class	TransposedConvolution
	Implementation of the Transposed Convolution class. More...
class	TripletMarginLoss
	The Triplet Margin Loss performance function measures the network's performance according to the relative distance from the anchor input of the positive (truthy) and negative (falsy) inputs. More...
class	ValidConvolution
class	VirtualBatchNorm
	Declaration of the VirtualBatchNorm layer class. More...
class	VRClassReward
	Implementation of the variance reduced classification reinforcement layer. More...
class	WeightNorm
	Declaration of the WeightNorm layer class. More...
class	WeightSetVisitor
	WeightSetVisitor update the module parameters given the parameters set. More...
class	WeightSizeVisitor
	WeightSizeVisitor returns the number of weights of the given module. More...
class	WGAN
	For more information, see the following paper: More...
class	WGANGP
	For more information, see the following paper: More...

Typedefs
using	XavierInitialization = GlorotInitializationType< true >
	XavierInitilization is the popular name for this method.
using	GlorotInitialization = GlorotInitializationType< false >
	GlorotInitialization uses uniform distribution.
template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	SigmoidLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Sigmoid-Layer using the logistic activation function.
template<class ActivationFunction = IdentityFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	IdentityLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Identity-Layer using the identity activation function.
template<class ActivationFunction = RectifierFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	ReLULayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard rectified linear unit non-linearity layer.
template<class ActivationFunction = TanhFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	TanHLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard hyperbolic tangent layer.
template<class ActivationFunction = SoftplusFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	SoftPlusLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Softplus-Layer using the Softplus activation function.
template<class ActivationFunction = HardSigmoidFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	HardSigmoidLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard HardSigmoid-Layer using the HardSigmoid activation function.
template<class ActivationFunction = SwishFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	SwishFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Swish-Layer using the Swish activation function.
template<class ActivationFunction = MishFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	MishFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Mish-Layer using the Mish activation function.
template<class ActivationFunction = LiSHTFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	LiSHTFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard LiSHT-Layer using the LiSHT activation function.
template<class ActivationFunction = GELUFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	GELUFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard GELU-Layer using the GELU activation function.
template<class ActivationFunction = ElliotFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	ElliotFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Elliot-Layer using the Elliot activation function.
template<class ActivationFunction = ElishFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	ElishFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard ELiSH-Layer using the ELiSH activation function.
template<class ActivationFunction = GaussianFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	GaussianFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Gaussian-Layer using the Gaussian activation function.
template<class ActivationFunction = HardSwishFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	HardSwishFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard HardSwish-Layer using the HardSwish activation function.
template<class ActivationFunction = TanhExpFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	TanhExpFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard TanhExp-Layer using the TanhExp activation function.
template<class ActivationFunction = SILUFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	SILUFunctionLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard SILU-Layer using the SILU activation function.
using	SELU = ELU< arma::mat, arma::mat >
using	MoreTypes = boost::variant< FlexibleReLU< arma::mat, arma::mat > *, Linear3D< arma::mat, arma::mat, NoRegularizer > *, LpPooling< arma::mat, arma::mat > *, PixelShuffle< arma::mat, arma::mat > *, ChannelShuffle< arma::mat, arma::mat > *, Glimpse< arma::mat, arma::mat > *, Highway< arma::mat, arma::mat > *, MultiheadAttention< arma::mat, arma::mat, NoRegularizer > *, Recurrent< arma::mat, arma::mat > *, RecurrentAttention< arma::mat, arma::mat > *, ReinforceNormal< arma::mat, arma::mat > *, ReLU6< arma::mat, arma::mat > *, Reparametrization< arma::mat, arma::mat > *, Select< arma::mat, arma::mat > *, SpatialDropout< arma::mat, arma::mat > *, Subview< arma::mat, arma::mat > *, VRClassReward< arma::mat, arma::mat > *, VirtualBatchNorm< arma::mat, arma::mat > *, RBF< arma::mat, arma::mat, GaussianFunction > *, BaseLayer< GaussianFunction, arma::mat, arma::mat > *, PositionalEncoding< arma::mat, arma::mat > *, ISRLU< arma::mat, arma::mat > *, NearestInterpolation< arma::mat, arma::mat > *>
template<typename... CustomLayers>
using	LayerTypes = boost::variant< AdaptiveMaxPooling< arma::mat, arma::mat > *, AdaptiveMeanPooling< arma::mat, arma::mat > *, Add< arma::mat, arma::mat > *, AddMerge< arma::mat, arma::mat > *, AlphaDropout< arma::mat, arma::mat > *, AtrousConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, BaseLayer< LogisticFunction, arma::mat, arma::mat > *, BaseLayer< IdentityFunction, arma::mat, arma::mat > *, BaseLayer< TanhFunction, arma::mat, arma::mat > *, BaseLayer< SoftplusFunction, arma::mat, arma::mat > *, BaseLayer< RectifierFunction, arma::mat, arma::mat > *, BatchNorm< arma::mat, arma::mat > *, BilinearInterpolation< arma::mat, arma::mat > *, CELU< arma::mat, arma::mat > *, Concat< arma::mat, arma::mat > *, Concatenate< arma::mat, arma::mat > *, ConcatPerformance< NegativeLogLikelihood< arma::mat, arma::mat >, arma::mat, arma::mat > *, Constant< arma::mat, arma::mat > *, Convolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, CReLU< arma::mat, arma::mat > *, DropConnect< arma::mat, arma::mat > *, Dropout< arma::mat, arma::mat > *, ELU< arma::mat, arma::mat > *, FastLSTM< arma::mat, arma::mat > *, GRU< arma::mat, arma::mat > *, HardTanH< arma::mat, arma::mat > *, Join< arma::mat, arma::mat > *, LayerNorm< arma::mat, arma::mat > *, LeakyReLU< arma::mat, arma::mat > *, Linear< arma::mat, arma::mat, NoRegularizer > *, LinearNoBias< arma::mat, arma::mat, NoRegularizer > *, LogSoftMax< arma::mat, arma::mat > *, Lookup< arma::mat, arma::mat > *, LSTM< arma::mat, arma::mat > *, MaxPooling< arma::mat, arma::mat > *, MeanPooling< arma::mat, arma::mat > *, MiniBatchDiscrimination< arma::mat, arma::mat > *, MultiplyConstant< arma::mat, arma::mat > *, MultiplyMerge< arma::mat, arma::mat > *, NegativeLogLikelihood< arma::mat, arma::mat > *, NoisyLinear< arma::mat, arma::mat > *, Padding< arma::mat, arma::mat > *, PReLU< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat, false > *, Sequential< arma::mat, arma::mat, true > *, Softmax< arma::mat, arma::mat > *, TransposedConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, WeightNorm< arma::mat, arma::mat > *, MoreTypes, CustomLayers *... >
template<typename MatType = arma::mat>
using	Embedding = Lookup< MatType, MatType >
template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, typename... CustomLayers>
using	Residual = Sequential< InputDataType, OutputDataType, true, CustomLayers... >
template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	CrossEntropyError = BCELoss< InputDataType, OutputDataType >
	Adding alias of BCELoss.
typedef LRegularizer< 1 >	L1Regularizer
	The L1 Regularizer.
typedef LRegularizer< 2 >	L2Regularizer
	The L2 Regularizer.
template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	CustomLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Sigmoid layer.

Functions
template<typename ModelType >
double	InceptionScore (ModelType Model, arma::mat images, size_t splits=1)
	Function that computes Inception Score for a set of images produced by a GAN. More...
	HAS_MEM_FUNC (Gradient, HasGradientCheck)
	HAS_MEM_FUNC (Deterministic, HasDeterministicCheck)
	HAS_MEM_FUNC (Parameters, HasParametersCheck)
	HAS_MEM_FUNC (Add, HasAddCheck)
	HAS_ANY_METHOD_FORM (Model, HasModelCheck)
	HAS_MEM_FUNC (Location, HasLocationCheck)
	HAS_MEM_FUNC (Reset, HasResetCheck)
	HAS_MEM_FUNC (ResetCell, HasResetCellCheck)
	HAS_MEM_FUNC (Reward, HasRewardCheck)
	HAS_MEM_FUNC (InputWidth, HasInputWidth)
	HAS_MEM_FUNC (InputHeight, HasInputHeight)
	HAS_MEM_FUNC (Rho, HasRho)
	HAS_MEM_FUNC (Loss, HasLoss)
	HAS_MEM_FUNC (Run, HasRunCheck)
	HAS_MEM_FUNC (Bias, HasBiasCheck)
	HAS_MEM_FUNC (MaxIterations, HasMaxIterations)
	HAS_ANY_METHOD_FORM (InputShape, HasInputShapeCheck)
template<typename T >
void	CheckInputShape (const T &network, const size_t inputShape, const std::string &functionName)

Detailed Description

Artificial Neural Network.

Artificial neural networks.

Artifical Neural Network.

Function Documentation

InceptionScore()

template<typename ModelType >

double mlpack::ann::InceptionScore	(	ModelType	Model,
		arma::mat	images,
		size_t	splits = 1
	)

Function that computes Inception Score for a set of images produced by a GAN.

For more information, see the following.

@article{Goodfellow2016,
  author  = {Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung,
             Alec Radford, Xi Chen},
  title   = {Improved Techniques for Training GANs},
  year    = {2016},
  url     = {https://arxiv.org/abs/1606.03498},
}

Parameters

Model	Model for evaluating the quality of images.
images	Images generated by GAN.
splits	Number of splits to perform (default: 1).