def run(): model = create_model(nin=11) backend = gen_backend(rng_seed=0) dataset = Axa() experiment = FitExperiment(model=model, backend=backend, dataset=dataset) experiment.run() outputs, targets = model.predict_fullset(dataset, 'test') print outputs.asnumpyarray()
class Network(object): def __init__(self, backend, dataset): layers = [] layers.append(DataLayer(nout=784)) layers.append(FCLayer(nout=1000, activation=RectLin())) layers.append(FCLayer(nout=10, activation=Logistic())) layers.append(CostLayer(cost=CrossEntropy())) self.model = MLP(num_epochs=10, batch_size=100, layers=layers) self.dataset = dataset def fit(self): self.experiment = FitExperiment(model=self.model, backend=backend, dataset=self.dataset) self.experiment.run() def predict(self): outputs, targets = self.model.predict_fullset(self.dataset, 'test') preds = np.argmax(outputs.asnumpyarray().T, axis=1) return preds
def fit(self): self.experiment = FitExperiment(model=self.model, backend=backend, dataset=self.dataset) self.experiment.run()