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_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_input_passes_through_network(self):
normalizer = Normalizer(in_max=10)
network = FeedForwardNN(normalizer, [2, 4, 5, 3])
network.receive_inputs([5.0, 7.1])
for output in network.output_layer:
self.assertNotAlmostEqual(output.output, 0.0)
sentSignals = [
connection.signal_sent for layer in network.layers for node in layer for connection in node.out_connections
]
self.assertTrue(s != 0.0 for s in sentSignals)
def test_calculation(self):
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])
neurons = network.neurons
outputs = network.receive_inputs([1])
self.assertEqual(len(outputs), 1)
# Nodes 1, 4, and 7 are bias nodes
self.assertAlmostEqual(neurons[0].output, 0.04742587318)
self.assertAlmostEqual(neurons[1].output, 1)
self.assertAlmostEqual(neurons[2].output, 0.7402802899)
self.assertAlmostEqual(neurons[3].output, 0.7402802899)
self.assertAlmostEqual(neurons[4].output, 1)
self.assertAlmostEqual(neurons[5].output, 0.9227677584)
self.assertAlmostEqual(neurons[6].output, 0.9227677584)
self.assertAlmostEqual(neurons[7].output, 1)
self.assertAlmostEqual(neurons[8].output, 0.9450874486)
self.assertAlmostEqual(outputs[0], 0.8901748973)
def test_teach_max_iterations(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, 2, 2, 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 = 123,
acceptable_error = 0)
self.assertEqual(result.epochs, 123)
def learn_function(fn, in_min, in_max):
expectations = [Expectation([i], fn(i)) for i in np.linspace(in_min, in_max + 1, num=100)]
in_range = in_max - in_min
in_min = in_min - 0.1 * in_range
in_max = in_max + 0.1 * in_range
out_min = None
out_max = None
for e in expectations:
if out_min == None or e.outputs[0] < out_min:
out_min = e.outputs[0]
if out_max == None or e.outputs[0] > out_max:
out_max = e.outputs[0]
out_range = out_max - out_min
out_min = out_min - 0.1 * out_range
out_max = out_max + 0.1 * out_range
backpropagator = Backpropagator()
normalizer = Normalizer(in_min=in_min, in_max=in_max, out_min=out_min, out_max=out_max, norm_min=-10, norm_max=10)
network = FeedForwardNN(normalizer, [1, 3, 3, 1], is_regression=True)
network.randomize_connection_weights(min=-1, max=1)
show_fit_tracker(network, expectations)
backpropagator.teach(
network,
expectations,
learning_rate=0.008,
max_iterations=30000,
acceptable_error=1,
callback_func=update_fit_plot,
)
return network
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)
