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## Uses of Interface
org.joone.engine.LearnableLayer

Packages that use LearnableLayer
org.joone.engine |
org.joone.net |

Uses of LearnableLayer in org.joone.engine

Classes in org.joone.engine that implement LearnableLayer
`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` | `Layer` The Layer object is the basic element forming the neural net.
`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.

Fields in org.joone.engine declared as LearnableLayer
`protected LearnableLayer` | `AbstractLearner.learnableLayer` The layer (biases) that is subjected to the learning process.

Methods in org.joone.engine that return LearnableLayer
`LearnableLayer` | `AbstractLearner.getLayer()` Gets the layer the learner is associated with.

Uses of LearnableLayer in org.joone.net

Classes in org.joone.net that implement LearnableLayer
`class` | `NestedNeuralLayer`

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