def _get_cascade(self, net):
net._output_connections[0][0] = 2.24107584
net._output_connections[0][1] = -0.00060484
net._output_connections[0][2] = -2.24107583
net._output_connections[0][3] = -2.24107584
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
return net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
def _get_cascade(self, net):
net._output_connections[0][0] = 2.24107584
net._output_connections[0][1] = -0.00060484
net._output_connections[0][2] = -2.24107583
net._output_connections[0][3] = -2.24107584
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
return net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
def test_correlation_maximize(self):
net = self.CLASS(2, 1)
net.use_quick_prop = True
net._output_connections[0][0] = -1.7
net._output_connections[0][1] = 1.7
net._output_connections[0][2] = -1.7
print_results(net, XOR_INPUTS, XOR_TARGETS)
# the error will be large for -1.0, 1.0 but small for everything else
winner = net._generate_candidate(XOR_INPUTS, XOR_TARGETS)
print(winner.activations)
self.assertAlmostEqual(winner.activations[2], max(winner.activations), delta=0.1)
def test_double_xor_cascading(self):
net = self.CLASS(3, 1, num_candidate_nodes=8)
net.learn_rate = 0.15
error = net.get_error(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
final_error = net.train(DOUBLE_XOR_INPUTS,
DOUBLE_XOR_TARGETS,
mini_batch_size=3,
max_hidden_nodes=10,
stop_error_threshold=1.0,
max_iterations_per_epoch=100)
print(len(net._cascade_connections))
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
self.assert_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
def test_correlation_maximize(self):
net = self.CLASS(2, 1)
net.use_quick_prop = True
net._output_connections[0][0] = -1.7
net._output_connections[0][1] = 1.7
net._output_connections[0][2] = -1.7
print_results(net, XOR_INPUTS, XOR_TARGETS)
# the error will be large for -1.0, 1.0 but small for everything else
winner = net._generate_candidate(XOR_INPUTS, XOR_TARGETS)
print(winner.activations)
self.assertAlmostEqual(winner.activations[2],
max(winner.activations),
delta=0.1)
def test_xor_cascading(self):
net = self.CLASS(2, 1)
net.momentum_coefficent = 0.7
net.learn_rate = 0.2
error = net.get_error(XOR_INPUTS, XOR_TARGETS)
final_error = net.train(XOR_INPUTS,
XOR_TARGETS,
mini_batch_size=1,
max_hidden_nodes=10,
stop_error_threshold=0.5,
max_iterations_per_epoch=50)
print_results(net, XOR_INPUTS, XOR_TARGETS)
self.assert_results(net, XOR_INPUTS, XOR_TARGETS)
self.assertGreater(error, final_error)
def test_double_xor_cascading(self):
net = self.CLASS(3, 1, num_candidate_nodes=8)
net.learn_rate = 0.15
error = net.get_error(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
final_error = net.train(
DOUBLE_XOR_INPUTS,
DOUBLE_XOR_TARGETS,
mini_batch_size=3,
max_hidden_nodes=10,
stop_error_threshold=1.0,
max_iterations_per_epoch=100,
)
print(len(net._cascade_connections))
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
self.assert_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
def test_xor_cascading(self):
net = self.CLASS(2, 1)
net.momentum_coefficent = 0.7
net.learn_rate = 0.2
error = net.get_error(XOR_INPUTS, XOR_TARGETS)
final_error = net.train(
XOR_INPUTS,
XOR_TARGETS,
mini_batch_size=1,
max_hidden_nodes=10,
stop_error_threshold=0.5,
max_iterations_per_epoch=50,
)
print_results(net, XOR_INPUTS, XOR_TARGETS)
self.assert_results(net, XOR_INPUTS, XOR_TARGETS)
self.assertGreater(error, final_error)
def test_generate_candidate(self):
net = self.CLASS(3, 1)
# this test keeps failing unless I set candidates to a very high number
# But I can't see a bug in the code...
net.num_candidate_nodes = 20
net._output_connections[0][0] = 2.24107584
net._output_connections[0][1] = -0.00060484
net._output_connections[0][2] = -2.24107583
net._output_connections[0][3] = -2.24107584
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
winner = net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
print(winner.activations)
print(winner.weights)
# This does not always hold true...
self.assertAlmostEqual(winner.activations[4], max(winner.activations), delta=0.1)
def test_generate_candidate(self):
net = self.CLASS(3, 1)
# this test keeps failing unless I set candidates to a very high number
# But I can't see a bug in the code...
net.num_candidate_nodes = 20
net._output_connections[0][0] = 2.24107584
net._output_connections[0][1] = -0.00060484
net._output_connections[0][2] = -2.24107583
net._output_connections[0][3] = -2.24107584
print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
winner = net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
print(winner.activations)
print(winner.weights)
# This does not always hold true...
self.assertAlmostEqual(winner.activations[4],
max(winner.activations),
delta=0.1)