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# `modules` – Structural Components: Modules

class `pybrain.structure.modules.``BiasUnit`(name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`

  A simple bias unit with a single constant output.

class `pybrain.structure.modules.``GaussianLayer`(dim, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`, `pybrain.structure.parametercontainer.ParameterContainer`

  A layer implementing a gaussian interpretation of the input. The mean is the input, the sigmas are stored in the module parameters.

  `setSigma`(sigma)
    Wrapper method to set the sigmas (the parameters of the module) to a certain value.

class `pybrain.structure.modules.``LinearLayer`(dim, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`

  The simplest kind of module, not doing any transformation.

  `__init__`(dim, name=None)
    Create a layer with dim number of units.

class `pybrain.structure.modules.``LSTMLayer`(dim, peepholes=False, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`, `pybrain.structure.parametercontainer.ParameterContainer`

  Long short-term memory cell layer.

  The input consists of 4 parts, in the following order: - input gate - forget gate - cell input - output gate

  `__init__`(dim, peepholes=False, name=None)

    Parameter: | dim – number of cells
    Key peepholes: | enable peephole connections (from state to gates)?

class `pybrain.structure.modules.``MDLSTMLayer`(dim, dimensions=1, peepholes=False, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`, `pybrain.structure.parametercontainer.ParameterContainer`

  Multi-dimensional long short-term memory cell layer.

  The cell-states are explicitly passed on through a part of the input/output buffers (which should be connected correctly with IdentityConnections).

  The input consists of 4 parts, in the following order: - input gate - forget gates (1 per dim) - cell input - output gate - previous states (1 per dim)

  The output consists of two parts: - cell output - current statte

  Attention: this module has to be used with care: it’s last <size> input and outputs are reserved for transmitting internal states on flattened recursive multi-dim networks, and so its connections have always to be sliced!

class `pybrain.structure.modules.``SigmoidLayer`(dim, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`

  Layer implementing the sigmoid squashing function.

  `__init__`(dim, name=None)
    Create a layer with dim number of units.

class `pybrain.structure.modules.``SoftmaxLayer`(dim, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`

  A layer implementing a softmax distribution over the input.

  `__init__`(dim, name=None)
    Create a layer with dim number of units.

class `pybrain.structure.modules.``StateDependentLayer`(dim, module, name=None, onesigma=True)
  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`, `pybrain.structure.parametercontainer.ParameterContainer`

class `pybrain.structure.modules.``TanhLayer`(dim, name=None)

  Bases: `pybrain.structure.modules.neuronlayer.NeuronLayer`

  A layer implementing the tanh squashing function.

  `__init__`(dim, name=None)
    Create a layer with dim number of units.

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