GenericFFNeuralNet

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def apply[Data, LayerP, I](trainingAlgorithm: GradBasedBackPropagation[LayerP, I], data: Data, trans: DataPipe[Data, Stream[(I, I)]], layerInitializer: RandomVariable[Seq[LayerP]]): GenericFFNeuralNet[Data, LayerP, I]

   

   Create a feed forward neural net

   Create a feed forward neural net

   trainingAlgorithm

   The optimization/training routine as a GradBasedBackPropagation instance

   data

   The training data

   trans

   A data pipeline transforming the training data from type Data to Stream of input patterns and targets

   layerInitializer

   A RandomVariable which generates samples for the layer parameters.

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10. final def getClass(): Class[_]

    

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11. def getWeightInitializer(num_units_by_layer: Seq[Int]): RandomVariable[Seq[(DenseMatrix[Double], DenseVector[Double])]]

    

    Returns a random variable which enables sampling of layer connection matrices, in the case of feed forward neural networks operating on breeze vectors.

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