def reproducible_network_train(seed=0, epochs=500, **additional_params):
""" Make a reproducible train for Gradient Descent based neural
network with a XOR problem and return trained network.
Parameters
----------
seed : int
Random State seed number for reproducibility. Defaults to ``0``.
epochs : int
Number of epochs for training. Defaults to ``500``.
**additional_params
Aditional parameters for Neural Network.
Returns
-------
GradientDescent instance
Returns trained network.
"""
np.random.seed(seed)
network = algorithms.GradientDescent(connection=[
layers.Tanh(2),
layers.Tanh(5),
layers.StepOutput(1, output_bounds=(-1, 1))
],
**additional_params)
network.train(xor_input_train, xor_target_train, epochs=epochs)
return network
def setUp(self):
super(LearningRateUpdatesTestCase, self).setUp()
self.first_step = 0.3
self.connection = [
layers.Tanh(2),
layers.Tanh(3),
layers.StepOutput(1, output_bounds=(-1, 1))
]
def test_that_alg_works(self):
network = algorithms.GradientDescent([
layers.Tanh(2),
layers.Tanh(3),
layers.StepOutput(1, output_bounds=(-1, 1))
],
step=0.3,
decay_rate=0.0001,
addons=[algorithms.WeightDecay])
network.train(xor_input_train, xor_target_train, epochs=500)
self.assertAlmostEqual(network.errors.last(), 0, places=2)
def test_that_alg_works(self):
network = algorithms.GradientDescent(
[
layers.Tanh(2),
layers.Tanh(3),
layers.StepOutput(1, output_bounds=(-1, 1))
],
step=0.3,
zero_weight=20,
addons=[algorithms.WeightElimination])
network.train(xor_input_train, xor_target_train, epochs=350)
self.assertAlmostEqual(network.errors.last(), 0, places=2)
def test_errdiff(self):
network = algorithms.GradientDescent(
[
layers.Tanh(2),
layers.Tanh(3),
layers.StepOutput(1, output_bounds=(-1, 1))
],
step=0.3,
update_for_smaller_error=1.05,
update_for_bigger_error=0.7,
error_difference=1.04,
addons=[algorithms.ErrDiffStepUpdate])
network.train(xor_input_train, xor_target_train, epochs=200)
self.assertAlmostEqual(network.errors.last(), 0, places=5)
def test_search_then_converge(self):
network = algorithms.GradientDescent(
[
layers.Tanh(2),
layers.Tanh(3),
layers.StepOutput(1, output_bounds=(-1, 1))
],
step=0.3,
epochs_step_minimizator=50,
rate_coefitient=0.2,
addons=[algorithms.SearchThenConverge])
network.train(xor_input_train, xor_target_train, epochs=6)
self.assertAlmostEqual(
network.variables.step.get_value(),
0.18,
places=5,
)