@Test public void when_callingIsRecurrent_expect_handlerIsCalled() { setup(false); sut.isRecurrent(); verify(handlerMock, times(1)).isRecurrent(); }
public boolean isRecurrent() { return networkHandler.isRecurrent(); }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { public boolean isRecurrent() { return networkHandler.isRecurrent(); } }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { public boolean isRecurrent() { return networkHandler.isRecurrent(); } protected  BaseNetwork(INetworkHandler networkHandler); }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { public boolean isRecurrent() { return networkHandler.isRecurrent(); } protected  BaseNetwork(INetworkHandler networkHandler); boolean isRecurrent(); @Override void fit(FeaturesLabels featuresLabels); @Override Gradients computeGradients(FeaturesLabels featuresLabels); @Override void applyGradients(Gradients gradients); @Override NeuralNetOutput output(Observation observation); NeuralNetOutput output(INDArray batch); @Override void reset(); void copyFrom(BaseNetwork from); abstract NET_TYPE clone(); }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { public boolean isRecurrent() { return networkHandler.isRecurrent(); } protected  BaseNetwork(INetworkHandler networkHandler); boolean isRecurrent(); @Override void fit(FeaturesLabels featuresLabels); @Override Gradients computeGradients(FeaturesLabels featuresLabels); @Override void applyGradients(Gradients gradients); @Override NeuralNetOutput output(Observation observation); NeuralNetOutput output(INDArray batch); @Override void reset(); void copyFrom(BaseNetwork from); abstract NET_TYPE clone();  }