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## Uses of Class
org.joone.engine.Layer

Packages that use Layer
org.joone.engine |
org.joone.net |
org.joone.structure |

Uses of Layer in org.joone.engine

Subclasses of Layer in org.joone.engine
`class` | `BiasedLinearLayer` This layer consists of linear neurons, i.e. neurons that sum up their inputs (actually this is done by the (full) synapse in Joone) along with their biases.
`class` | `ContextLayer` The context layer is similar to the linear layer except that it has an auto-recurrent connection between its output and input.
`class` | `DelayLayer` Delay unit to create temporal windows from time series `O---> Yk(t-N) | ...`
`class` | `GaussianLayer` This layer implements the Gaussian Neighborhood SOM strategy.
`class` | `GaussLayer` The output of a Gauss(ian) layer neuron is the sum of the weighted input values, applied to a gaussian curve (`exp(- x * x)`).
`class` | `LinearLayer` The output of a linear layer neuron is the sum of the weighted input values, scaled by the beta parameter.
`class` | `LogarithmicLayer` This layer implements a logarithmic transfer function.
`class` | `MemoryLayer`
`class` | `RbfGaussianLayer` This class implements the nonlinear layer in Radial Basis Function (RBF) networks using Gaussian functions.
`class` | `RbfLayer` This is the basis (helper) for radial basis function layers.
`class` | `SigmoidLayer` The output of a sigmoid layer neuron is the sum of the weighted input values, applied to a sigmoid function.
`class` | `SimpleLayer` This abstract class represents layers that are composed by neurons that implement some transfer function.
`class` | `SineLayer` The output of a sine layer neuron is the sum of the weighted input values, applied to a sine (`sin(x)`).
`class` | `SoftmaxLayer` The outputs of the Softmax layer must be interpreted as probabilities.
`class` | `TanhLayer` Layer that applies the tangent hyperbolic transfer function to its input patterns
`class` | `WTALayer` This layer implements the Winner Takes All SOM strategy.

Uses of Layer in org.joone.net

Subclasses of Layer in org.joone.net
`class` | `NestedNeuralLayer`

Methods in org.joone.net that return Layer
`Layer[]` | `NeuralNet.calculateOrderedLayers()` This method calculates the order of the layers of the network, from the input to the output.
`Layer` | `NeuralNet.findInputLayer()` Returns the input layer, by searching for it following the rules written in Layer.isInputLayer.
`Layer` | `NeuralNet.findOutputLayer()` Returns the output layer by searching for it following the rules written in Layer.isOutputLayer.
`Layer` | `NeuralNet.getInputLayer()` Returns the input layer of the network.
`Layer` | `NeuralNet.getLayer(java.lang.String layerName)`
`Layer[]` | `NeuralNet.getOrderedLayers()`
`Layer` | `NeuralNet.getOutputLayer()` Returns the output layer of the network.

Methods in org.joone.net with parameters of type Layer
`void` | `NeuralNet.addLayer(Layer layer)`
`void` | `NeuralNet.addLayer(Layer layer, int tier)`
`void` | `NeuralNet.removeLayer(Layer layer)`
`void` | `NeuralNet.setInputLayer(Layer newLayer)`
`void` | `NeuralNet.setOrderedLayers(Layer[] orderedLayers)` This method permits to set externally a particular order to traverse the Layers.
`void` | `NeuralNet.setOutputLayer(Layer newLayer)`

Uses of Layer in org.joone.structure

Methods in org.joone.structure that return Layer
`protected Layer` | `Nakayama.findInputLayer(Synapse aSynapse)` Finds the input layer of a synapse.
`protected Layer` | `Nakayama.findOutputLayer(Synapse aSynapse)` Finds the output layer of a synapse.

Methods in org.joone.structure with parameters of type Layer
`void` | `Nakayama.addLayer(Layer aLayer)` Adds layers to this optimizer.
`protected double` | `Nakayama.getSumAbsoluteWeights(Layer aLayer, int aNeuron)` Sums up all the absolute values of the output weights of a neuron within a layer.

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