def test_dcae():
"""
Tests that DeepComposedAutoencoder calls the Model superclass constructor
"""
ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos',
tied_weights=True)
model = DeepComposedAutoencoder([ae])
model._ensure_extensions()
def test_dcae():
"""
Tests that DeepComposedAutoencoder works correctly
"""
ae = Autoencoder(5, 7, act_enc="tanh", act_dec="cos", tied_weights=True)
model = DeepComposedAutoencoder([ae])
model._ensure_extensions()
data = np.random.randn(10, 5).astype(config.floatX)
model.perform(data)
def train_model():
global ninput, noutput
simdata = SimulationData(
sim_path="../../javaDataCenter/generarDadesV1/CA_SDN_topo1/")
simdata.load_data()
simdata.preprocessor()
dataset = simdata.get_matrix()
structure = get_structure()
layers = []
for pair in structure:
layers.append(get_autoencoder(pair))
model = DeepComposedAutoencoder(layers)
training_alg = SGD(learning_rate=1e-3,
cost=MeanSquaredReconstructionError(),
batch_size=1296,
monitoring_dataset=dataset,
termination_criterion=EpochCounter(max_epochs=50))
extensions = [MonitorBasedLRAdjuster()]
experiment = Train(dataset=dataset,
model=model,
algorithm=training_alg,
save_path='training2.pkl',
save_freq=10,
allow_overwrite=True,
extensions=extensions)
experiment.main_loop()
def test_dcae():
"""
Tests that DeepComposedAutoencoder works correctly
"""
ae = Autoencoder(5, 7, act_enc='tanh', act_dec='cos', tied_weights=True)
model = DeepComposedAutoencoder([ae])
model._ensure_extensions()
data = np.random.randn(10, 5).astype(config.floatX)
model.perform(data)
def get_model(self): self.model = DeepComposedAutoencoder(self.layers) return self.model
