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()
