Man pages for darch
Package for Deep Architectures and Restricted Boltzmann Machines
| addLayer | Adds a layer to the 'DArch' object |
| addLayer-DArch-method | Adds a layer to the 'DArch' object |
| addLayerField | Adds a field to a layer |
| addLayerField-DArch-method | Adds a field to a layer |
| backpropagation | Backpropagation learning function |
| contr.ltfr | Wrapper for 'contr.ltfr' |
| createDataSet | Create data set using data, targets, a formula, and possibly... |
| createDataSet-ANY-ANY-missing-DataSet-method | Create new 'DataSet' by filling an existing one with new... |
| createDataSet-ANY-ANY-missing-missing-method | Create 'DataSet' using data and targets. |
| createDataSet-ANY-missing-formula-missing-method | Constructor function for 'DataSet' objects. |
| crossEntropyError | Cross entropy error function |
| darch | Fit a deep neural network |
| darchBench | Benchmarking wrapper for 'darch' |
| DArch-class | Class for deep architectures |
| darchModelInfo | Creates a custom caret model for 'darch'. |
| darchTest | Test classification network. |
| DataSet | Class for specifying datasets. |
| exponentialLinearUnit | Exponential linear unit (ELU) function with unit derivatives. |
| fineTuneDArch | Fine tuning function for the deep architecture |
| fineTuneDArch-DArch-method | Fine tuning function for the deep architecture |
| generateDropoutMask | Dropout mask generator function. |
| generateRBMs-DArch-method | Generates the RBMs for the pre-training. |
| generateWeightsGlorotNormal | Glorot normal weight initialization |
| generateWeightsGlorotUniform | Glorot uniform weight initialization |
| generateWeightsHeNormal | He normal weight initialization |
| generateWeightsHeUniform | He uniform weight initialization |
| generateWeightsNormal | Generates a weight matrix using rnorm. |
| generateWeightsUniform | Generates a weight matrix using runif |
| getDropoutMask | Returns the dropout mask for the given layer |
| getMomentum | Returns the current momentum of the 'Net'. |
| linearUnit | Linear unit function with unit derivatives. |
| linearUnitRbm | Calculates the linear neuron output no transfer function |
| loadDArch | Loads a DArch network |
| makeStartEndPoints | Makes start- and end-points for the batches. |
| maxoutUnit | Maxout / LWTA unit function |
| maxoutWeightUpdate | Updates the weight on maxout layers |
| minimize | Minimize a differentiable multivariate function. |
| minimizeAutoencoder | Conjugate gradient for a autoencoder network |
| minimizeClassifier | Conjugate gradient for a classification network |
| mseError | Mean squared error function |
| Net | Abstract class for neural networks. |
| newDArch | Constructor function for 'DArch' objects. |
| plot.DArch | Plot 'DArch' statistics or structure. |
| predict.DArch | Forward-propagate data. |
| preTrainDArch | Pre-trains a 'DArch' network |
| preTrainDArch-DArch-method | Pre-trains a 'DArch' network |
| print.DArch | Print 'DArch' details. |
| provideMNIST | Provides MNIST data set in the given folder. |
| RBM | Class for restricted Boltzmann machines |
| rbmUpdate | Function for updating the weights and biases of an 'RBM' |
| readMNIST | Function for generating .RData files of the MNIST Database |
| rectifiedLinearUnit | Rectified linear unit function with unit derivatives. |
| resetRBM | Resets the weights and biases of the 'RBM' object |
| rmseError | Root-mean-square error function |
| rpropagation | Resilient backpropagation training for deep architectures. |
| runDArch | Forward-propagates data through the network |
| runDArchDropout | Forward-propagate data through the network with dropout... |
| saveDArch | Saves a DArch network |
| setDarchParams | Set 'DArch' parameters |
| setDropoutMask-set | Set the dropout mask for the given layer. |
| setLogLevel | Set the log level. |
| show-DArch-method | Print 'DArch' details. |
| sigmoidUnit | Sigmoid unit function with unit derivatives. |
| sigmoidUnitRbm | Calculates the RBM neuron output with the sigmoid function |
| softmaxUnit | Softmax unit function with unit derivatives. |
| softplusUnit | Softplus unit function with unit derivatives. |
| tanhUnit | Continuous Tan-Sigmoid unit function. |
| tanhUnitRbm | Calculates the neuron output with the hyperbolic tangent... |
| trainRBM | Trains an 'RBM' with contrastive divergence |
| validateDataSet | Validate 'DataSet' |
| validateDataSet-DataSet-method | Validate 'DataSet' |
| weightDecayWeightUpdate | Updates the weight using weight decay. |
