@Test public void when_callingFit_expect_handlerIsCalled() { setup(false); FeaturesLabels featuresLabels = new FeaturesLabels(null); sut.fit(featuresLabels); verify(handlerMock, times(1)).performFit(featuresLabels); }
@Override public void fit(FeaturesLabels featuresLabels) { invalidateCache(); networkHandler.performFit(featuresLabels); }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { @Override public void fit(FeaturesLabels featuresLabels) { invalidateCache(); networkHandler.performFit(featuresLabels); } }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { @Override public void fit(FeaturesLabels featuresLabels) { invalidateCache(); networkHandler.performFit(featuresLabels); } protected  BaseNetwork(INetworkHandler networkHandler); }
BaseNetwork implements ITrainableNeuralNet<NET_TYPE> { @Override public void fit(FeaturesLabels featuresLabels) { invalidateCache(); networkHandler.performFit(featuresLabels); } 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> { @Override public void fit(FeaturesLabels featuresLabels) { invalidateCache(); networkHandler.performFit(featuresLabels); } 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();  }