Examples of FlatNetwork

org.encog.engine.network.flat.FlatNetwork
Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, code reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.
org.encog.neural.flat.FlatNetwork
Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, code reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.

Examples of org.encog.neural.flat.FlatNetwork

    network.reset();
    
    network.setLayerBiasActivation(0,0.5);
    network.setLayerBiasActivation(1,-1.0);
    
    FlatNetwork flat = network.getStructure().getFlat();
    
    Assert.assertNotNull(flat);
    double[] layerOutput = flat.getLayerOutput();
    Assert.assertEquals(layerOutput[5], -1.0);
    Assert.assertEquals(layerOutput[8], 0.5);  
  }

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