def test_adagrad(self):
x_train, x_test, y_train, y_test = simple_classification()
optimizer = algorithms.Adagrad(
self.network,
step=0.1,
batch_size=None,
verbose=False,
)
optimizer.train(x_train, y_train, x_test, y_test, epochs=150)
self.assertGreater(0.15, optimizer.errors.valid[-1])
def test_simple_adagrad(self):
x_train, _, y_train, _ = simple_classification()
mnet = algorithms.Adagrad(
(10, 20, 1),
step=2.,
batch_size='full',
verbose=False,
epsilon=1e-5,
)
mnet.train(x_train, y_train, epochs=100)
self.assertAlmostEqual(0.068, mnet.errors.last(), places=3)
def test_simple_adagrad(self):
x_train, x_test, y_train, y_test = simple_classification()
mnet = algorithms.Adagrad(
(10, 20, 1),
step=0.1,
batch_size='full',
verbose=False,
epsilon=1e-5,
)
mnet.train(x_train, y_train, x_test, y_test, epochs=100)
self.assertGreater(0.15, mnet.validation_errors.last())
def select_algorithm(self, algorithm, options=None):
try:
self.network = algorithms.LevenbergMarquardt(self.layers)
opt = options
print(opt[1])
print("Wybrano optymalizator: " + str(algorithm))
except RecursionError:
print("Problem rekursji")
return None
if algorithm == 'GradientDescent':
self.network = algorithms.GradientDescent(self.layers)
if algorithm == 'LevenbergMarquardt':
self.network = algorithms.LevenbergMarquardt(connection=self.layers, mu=opt[0], mu_update_factor=opt[1])
if algorithm == 'Adam':
self.network = algorithms.Adam(self.layers)
if algorithm == 'QuasiNewton':
self.network = algorithms.QuasiNewton(self.layers)
if algorithm == 'Quickprop':
self.network = algorithms.Quickprop(self.layers)
if algorithm == 'MinibatchGradientDescent':
self.network = algorithms.MinibatchGradientDescent(self.layers)
if algorithm == 'ConjugateGradient':
self.network = algorithms.ConjugateGradient(self.layers)
if algorithm == 'Hessian':
self.network = algorithms.Hessian(self.layers)
if algorithm == 'HessianDiagonal':
self.network = algorithms.HessianDiagonal(self.layers)
if algorithm == 'Momentum':
self.network = algorithms.Momentum(self.layers)
if algorithm == 'RPROP':
self.network = algorithms.RPROP(self.layers)
if algorithm == 'IRPROPPlus':
self.network = algorithms.IRPROPPlus(self.layers)
if algorithm == 'Adadelta':
self.network = algorithms.Adadelta(self.layers)
if algorithm == 'Adagrad':
self.network = algorithms.Adagrad(self.layers)
if algorithm == 'RMSProp':
self.network = algorithms.RMSProp(self.layers)
if algorithm == 'Adamax':
self.network = algorithms.Adamax(self.layers)