def test_propagate_errors(self):
backpropagator = Backpropagator()
normalizer = Normalizer(in_max = 100, out_max = 200)
network = FeedForwardNN(normalizer, [1, 2, 1])
expectation = Expectation([50], [148])
result = network.receive_inputs(expectation.inputs)
backpropagator.learn(network, expectation, 1)
def test_learn(self):
backpropagator = Backpropagator()
normalizer = Normalizer(in_min = 1, in_max = 15,
out_min = -1, out_max = 1,
norm_min = -3, norm_max = 3)
network = FeedForwardNN(normalizer, [1, 2, 2, 1])
network.randomize_connection_weights(seed = 74)
neurons = network.neurons
expectation = Expectation([1], [0.8415])
error = backpropagator.calculate_error(
network.receive_inputs(expectation.inputs),
expectation.outputs)
for i in range(20):
last_error = error
backpropagator.learn(network, expectation, 1.5)
actual = network.receive_inputs(expectation.inputs)
print(actual)
error = backpropagator.calculate_error(actual, expectation.outputs)
self.assertLess(error, last_error)
