def test_teach_acceptable_error(self):
backpropagator = Backpropagator()
normalizer = Normalizer(in_min = -15, in_max = 15,
out_min = -30, out_max = 30,
norm_min = -2, norm_max = 2)
network = FeedForwardNN(normalizer, [1, 3, 1])
network.randomize_connection_weights(seed = 74)
expectations = [Expectation([i], [2*i])
for i in range(-5, 5)]
result = backpropagator.teach(network,
expectations,
learning_rate = 1.5,
max_iterations = 2000,
acceptable_error = .5)
self.assertLessEqual(result.error, .5)
errors = 0
for exp in expectations:
errors += backpropagator.calculate_error(
network.receive_inputs(exp.inputs), exp.outputs)
self.assertLessEqual(errors, .5)
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)
def test_calculate_error(self):
backpropagator = Backpropagator()
error = backpropagator.calculate_error([5.5, 1, -8], [5, -4, 2.5])
self.assertAlmostEqual(error, 135.5)
