def test_network_feed_forward_computes_units_by_layer():
network = NeuralNetwork(layers=[2, 3, 2])
network.weights = [np.ones((3, 3)), np.ones((2, 4))]
a_, z_ = network.feed_forward([4, 6])
assert len(z_) == len(a_)
assert len(z_) == network.num_layers
assert len(a_[0]) == 2
assert len(z_[1]) == len(a_[1])
assert len(z_[1]) == 3
assert len(z_[2]) == len(a_[2])
assert len(z_[2]) == 2
def test_network_backpropagation():
network = NeuralNetwork(layers=[2, 3, 1])
network.weights = [
np.array([[1, 1, 2], [1, 2, 1], [1, 1, 1]]),
np.ones((1, 4))
]
x = np.array([[2, 3], [1, 1]])
y = np.array([2, 1]).reshape(2, 1)
gradients = network.backpropagation(x, y)
assert len(gradients) == network.num_layers - 1
assert np.shape(gradients[0]) == (3, 3)
assert np.shape(gradients[1]) == (1, 4)
def test_network_feed_forward_computes_hidden_units():
network = NeuralNetwork(layers=[2, 3, 2])
network.weights = [
np.array([[1, 1, 2], [1, 2, 1], [1, 1, 1]]),
np.ones((2, 4))
]
a_, z_ = network.feed_forward(np.array([2, 3]))
np.testing.assert_array_equal(a_[0], np.array([2, 3]))
np.testing.assert_array_equal(z_[1], np.array([9, 8, 6]))
np.testing.assert_array_equal(
a_[1], np.array([sigmoid(9), sigmoid(8),
sigmoid(6)]))
value = sigmoid(9) + sigmoid(8) + sigmoid(
6) + 1 # weights of theta2 are 1's
np.testing.assert_array_equal(z_[2], np.array([value, value]))