Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12345]

boost
serialization
mic
application
applications
MNISTPatchReconstructionApplication	Class implementing a simple MNIST patch reconstruction with multi-layer neural net
MNISTPatchSoftmaxApplication	Class implementing a simple MNIST patch softmax classification with multi-layer neural net - imported from previously loaded auto-encoder net and adds softmax layer "at the top"
mlnn
activation_function
ELU	Class implementing the layer with Exponential Linear Unit (ELU). http://arxiv.org/pdf/1511.07289v5.pdf
ReLU
Sigmoid
convolution
Convolution	Class representing a convolution layer, with "valid padding" and variable stride
Cropping	Class implementing cropping operation - crops the size of image (matrix) by a margin of n pixels on every image side/channel
MaxPooling	Layer performing max pooling
Padding	Class implementing padding operation - expanding the size of image (matrix) by a margin of n pixels on every image side
cost_function
Softmax	Softmax activation function
experimental
ConvHebbian	Class implementing a convolutional hebbian layer
fully_connected
BinaryCorrelator	Class implementing a linear, fully connected layer
HebbianLinear	Class implementing a linear, fully connected layer
SparseLinear	Class implementing a linear, fully connected layer with sparsity regulation
Linear	Class implementing a linear, fully connected layer
regularisation
Dropout	Droput layer - a layer used for the regularization of neural network by randomly dropping neurons during training
BackpropagationNeuralNetwork	Class representing a multi-layer neural network based on backpropagation/gradient descent
HebbianNeuralNetwork	Class representing a multi-layer neural network based on hebbian learning
MultiLayerNeuralNetwork	Class representing a multi-layer neural network
Layer
neural_nets
learning
BinaryCorrelatorLearningRule	Updates according to classical Hebbian rule (wij += ni * x * y)
HebbianRule	Updates according to classical Hebbian rule (wij += ni * x * y)
NormalizedHebbianRule	Updates according to classical Hebbian rule (wij += ni * x * y) with additional normalization
NormalizedZerosumHebbianRule	Updates according to a modified Hebbian rule (wij += ni * f(x, y)) with additional normalization and zero summing for optimal edge detection
loss
CrossEntropyLoss	Class representing a cross-entropy loss function (classification)
LogLikelihoodLoss	Class representing a log-likelihood cost (to be used with softmax logistic regression)
Loss	Abstract class representing a loss function. Defines interfaces
SquaredErrorLoss	Class representing a squared error loss function (regression). L = 1/2 sum (t - p)^2
optimization
artificial_landscapes
DifferentiableFunction	Abstract class representing interface to a differentiable function
SphereFunction	A sphere function - square function generalized to n dimensions
Beale2DFunction	2D Beale's function
Rosenbrock2DFunction	2D Rosenbrock function
AdaDelta	Update using AdaDelta - adaptive gradient descent with running average E[g^2] and E[d^2]
AdaGrad	Update using AdaGrad - adaptive gradient descent
Adam	Adam - adaptive moment estimation
AdamID	AdamID - ADAM with integral and derivative coefficients
GradientDescent	Update in the direction of gradient descent
GradPID	GradPID - adaptive gradient descent with proportional, integral and derivative coefficients
AdaGradPID	AdaGradPID - adaptive gradient descent with proportional, integral and derivative coefficients
Momentum	Update in the direction of gradient descent - with momentum
OptimizationArray	A dynamic array of optimization functions (a hash-table)
OptimizationFunction	Abstract class representing interface to optimization function
RMSProp	Update using RMSProp - adaptive gradient descent with running average E[g^2]
unit_tests
Conv2x2x2Filter2x1x1s1Double	Test Fixture - layer of input size 2x2x2 and with filter bank of 2 filters of size 1x1 with stride 1, double. Math example taken from my own calculations;)
Conv3x3x2Filter3x2x2s1Float	Test Fixture - layer of input size 3x3x2 and with filter bank of 3 filters of size 2x2 with stride 1, floats. Math example taken from my whiteboard;)
Conv4x4x1Filter1x2x2s2Float	Test Fixture - layer of input size 4x4x1 and with filter bank of 1 filters of size 2x2 with stride 2, floats. Math example taken from my own YET ANOTHER calculations! ech!
Conv4x4x1Filter3x1x1s3Double	Test Fixture - layer of input size 4x4x1 and with filter bank of 3 filters of size 1x1 with stride 3, double. Math example taken from my own calculations;)
Conv5x5x1Filter1x3x3s1Float	Test Fixture - layer of input size 5x5x1 and with filter bank of 1 filter of size 3x3 with stride 1 (floats). Math example taken from: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
Conv5x5x1Filter1x2x2s3Float	Test Fixture - layer of input size 5x5x1 and with filter bank of 1 filter of size 2x2 with stride 3 (float). Math example taken from: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
Conv7x7x3Filter3x3x3s2Float	Test Fixture - layer of input size 7x7x3 and with filter bank of 2 filters of 3x3 with stride 2 (floats). Math example taken from: http://cs231n.github.io/convolutional-networks/
Conv5x6x1Filter1x4x4s1Float	Test Fixture - layer of input size 5x6x1 and with filter bank of 1 filter of size 4x4 with stride 1, floats. Math example taken from: http://soumith.ch/ex/pages/2014/08/07/why-rotate-weights-convolution-gradient/
Conv28x28x1Filter2x28x28s1Double	Test Fixture - layer of input size 28x28x1 and with filter bank of 2 filters of size 28x28 with stride 1, double
Conv8x8x1Filter2x4x4s4Double	Test Fixture - layer of input size 8x8x1 and with filter bank of 2 filters of size 4x4 with stride 4, double
Linear1x1Float	Test Fixture - layer of size 1x1, floats, sets W[0] = 1.0 and b[0] = 1.0
Linear5x2Float	Test Fixture - layer of size 5x2, floats
Linear2x3Float	Test Fixture - layer of size 2x3, floats, sets all internal and external values
Linear2x3Double	Test Fixture - layer of size 2x3, doubles, sets all internal and external values
Linear50x100Double	Test Fixture - layer of size 50x100, doubles, randomly sets all internal and external values required for numerical gradient verification
Simple2LayerRegressionNN	Test Fixture - simple ff net with 2 layers
Tutorial2LayerNN	Test Fixture - feed-forward net with 2 layers. A "formalized" example from a step-by-step tutorial: https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
Beale2DLandscape	Test fixture - artificial landscape - Beale's function 2D
Rosenbrock2DLandscape	Test fixture - artificial landscape - Rosenbrock function 2D
Softmax4x1Float	Test Fixture - 4x1 softmax layer
Sphere1DLandscape	Test fixture - artificial landscape - sphere function 1D (square function)
Sphere20DLandscape	Test fixture - artificial landscape - sphere function 20D (square function)
Vectors3x2Float	Test Fixture - two vectors of size 3x2, floats
Vectors4x1Float	Test Fixture - two vectors of size 4x1, floats
Vectors4x1Float2	Test Fixture - two predictions of size 4x1, floats