def test_train_with_generated_data_then_predict(docknet1: Docknet):
np.random.seed(1)
data_generator = DummyDataGenerator()
samples, labels = data_generator.generate_balanced_shuffled_sample(8)
docknet1.train(samples, labels, batch_size=4, max_number_of_epochs=40)
predicted_labels = docknet1.predict(samples)
predicted_labels = np.round(predicted_labels)
assert_array_almost_equal(predicted_labels, labels)
def test_to_json(docknet1: Docknet):
# Set network parameters as for the dummy initializer in order to enforce a specific expected output
docknet1.initializer.initialize(docknet1.layers)
expected_path = os.path.join(data_dir, 'docknet1.json')
with open(expected_path, 'rt', encoding='UTF-8') as fp:
expected = fp.read()
actual_file = io.StringIO()
docknet1.to_json(actual_file, True)
actual = actual_file.getvalue()
assert actual == expected
def test_train(docknet1: Docknet):
docknet1.train(X, Y, batch_size=2, max_number_of_epochs=1)
expected_optimized_W1 = optimized_W1
expected_optimized_b1 = optimized_b1
expected_optimized_W2 = optimized_W2
expected_optimized_b2 = optimized_b2
actual_optimized_W1 = docknet1.layers[1].params['W']
actual_optimized_b1 = docknet1.layers[1].params['b']
actual_optimized_W2 = docknet1.layers[2].params['W']
actual_optimized_b2 = docknet1.layers[2].params['b']
assert_array_almost_equal(actual_optimized_W1, expected_optimized_W1)
assert_array_almost_equal(actual_optimized_b1, expected_optimized_b1)
assert_array_almost_equal(actual_optimized_W2, expected_optimized_W2)
assert_array_almost_equal(actual_optimized_b2, expected_optimized_b2)
def docknet2():
docknet1 = Docknet()
docknet1.add_input_layer(2)
docknet1.add_dense_layer(3, 'relu')
docknet1.add_dense_layer(1, 'sigmoid')
docknet1.cost_function = 'cross_entropy'
docknet1.initializer = DummyInitializer()
docknet1.optimizer = AdamOptimizer()
yield docknet1
def test_predict(docknet1: Docknet):
# Set network parameters as for the dummy initializer in order to enforce a specific expected output
docknet1.initializer.initialize(docknet1.layers)
expected = Y_circ
actual = docknet1.predict(X)
assert_array_almost_equal(actual, expected)
def docknet1() -> Docknet:
docknet1 = Docknet()
docknet1.add_input_layer(2)
docknet1.add_dense_layer(3, 'relu')
docknet1.add_dense_layer(1, 'sigmoid')
docknet1.cost_function = 'cross_entropy'
docknet1.initializer = DummyInitializer()
docknet1.optimizer = GradientDescentOptimizer()
yield docknet1