@Test public void testGetLayersCount(){ assertEquals("Count of neural network layers is not as expected.", NET_TOPOLOGY.length , neuralNet.getLayersCount()); }
public int getLayersCount() { return layers.size(); }
BasicNeuralNet implements NeuralNet, Serializable { public int getLayersCount() { return layers.size(); } }
BasicNeuralNet implements NeuralNet, Serializable { public int getLayersCount() { return layers.size(); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); }
BasicNeuralNet implements NeuralNet, Serializable { public int getLayersCount() { return layers.size(); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); List<Neuron> getLayerUnits(int layerIndex); int getLayersCount(); double[] feedForward(final double[] inputPattern); double[] getOutput(); void reset(); int getNetInputSize(); int getNetOutputSize(); }
BasicNeuralNet implements NeuralNet, Serializable { public int getLayersCount() { return layers.size(); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); List<Neuron> getLayerUnits(int layerIndex); int getLayersCount(); double[] feedForward(final double[] inputPattern); double[] getOutput(); void reset(); int getNetInputSize(); int getNetOutputSize();  }