def test_forward_pass(self):
input_dim = 2
number_labels = 2
intermediate_neurons = 3
intermediate_layers = 1
neural_net = NeuralNetwork(input_dim=input_dim,
number_labels=number_labels,
intermediate_neurons=intermediate_neurons,
intermediate_layers=intermediate_layers)
# the layers in the neural net are randomly initialized
# modify them to fixed numbers in order to test
first_layer = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
intermediate_layer = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
intermediate_layer = np.vstack((intermediate_layer, np.array([1, 1,
1]).T))
last_layer = np.array([[0.5, 1], [0.5, 1], [0.5, 1], [1, 1]])
neural_net.layers = [first_layer, intermediate_layer, last_layer]
expected_result = [92.5, 184]
actual_result = neural_net.forward_pass(np.array([1, 3]))
for actual, expected in zip(actual_result, expected_result):
self.assertEqual(expected, actual)
def test_errors_computed_correctly(self):
# mock the layers just as in the forward pass case
input_dim = 2
number_labels = 2
intermediate_neurons = 3
intermediate_layers = 1
neural_net = NeuralNetwork(input_dim=input_dim,
number_labels=number_labels,
intermediate_neurons=intermediate_neurons,
intermediate_layers=intermediate_layers)
# the layers in the neural net are randomly initialized
# modify them to fixed numbers in order to test
first_layer = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
intermediate_layer = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])
intermediate_layer = np.vstack((intermediate_layer, np.array([1, 1,
1]).T))
last_layer = np.array([[0.5, 1], [0.5, 1], [0.5, 1], [1, 1]])
neural_net.layers = [first_layer, intermediate_layer, last_layer]
