def test_backward_propagation(self):
# test example from coursera deeplearning.ai
np.random.seed(3)
AL = cp.array(np.random.randn(1, 2))
Y = cp.array([[1, 0]])
dA = -(Y / AL - (1 - Y) / (1 - AL))
A1 = cp.array(np.random.randn(4, 2))
W1 = cp.array(np.random.randn(3, 4))
b1 = cp.array(np.random.randn(3, 1))
Z1 = cp.array(np.random.randn(3, 2))
A2 = cp.array(np.random.randn(3, 2))
W2 = cp.array(np.random.randn(1, 3))
b2 = cp.array(np.random.randn(1, 1))
Z2 = cp.array(np.random.randn(1, 2))
l1 = FCLayer(4, 3, activation='relu')
l1.Z = Z1
l1.W = W1
l1.b = b1
l1.X = A1
l2 = FCLayer(3, 2, activation='sigmoid')
l2.Z = Z2
l2.W = W2
l2.b = b2
l2.X = A2
dA = l2.backward(dA)
dA = l1.backward(dA)
self.assertAlmostEqual(0.41010002, float(l1.dW[0, 0]), places=7)
self.assertAlmostEqual(0.01005865, float(l1.dW[2, 1]), places=7)
self.assertAlmostEqual(-0.02835349, float(l1.db[2, 0]), places=7)
def test_update_parameters(self):
# test example from coursera deeplearning.ai
np.random.seed(1)
W1 = cp.array(np.random.randn(2, 3))
b1 = cp.array(np.random.randn(2, 1))
W2 = cp.array(np.random.randn(3, 3))
b2 = cp.array(np.random.randn(3, 1))
dW1 = cp.array(np.random.randn(2, 3))
db1 = cp.array(np.random.randn(2, 1))
dW2 = cp.array(np.random.randn(3, 3))
db2 = cp.array(np.random.randn(3, 1))
l1 = FCLayer(3, 2)
l1.W = W1
l1.b = b1
l1.dW = dW1
l1.db = db1
l2 = FCLayer(3, 3)
l2.W = W2
l2.b = b2
l2.dW = dW2
l2.db = db2
l1.update_parameters(0.01, 2)
l2.update_parameters(0.01, 2)
self.assertAlmostEqual(float(l1.W[0, 0]), 1.63178673)
self.assertAlmostEqual(float(l2.sb[0, 0]), 5.49507194e-05)
self.assertAlmostEqual(float(l1.b[1, 0]), -0.75376553)
def test_forward_propagation(self):
# test example from coursera deeplearning.ai
np.random.seed(6)
X = cp.array(np.random.randn(5, 4))
W1 = cp.array(np.random.randn(4, 5))
b1 = cp.array(np.random.randn(4, 1))
W2 = cp.array(np.random.randn(3, 4))
b2 = cp.array(np.random.randn(3, 1))
W3 = cp.array(np.random.randn(1, 3))
b3 = cp.array(np.random.randn(1, 1))
lay1 = FCLayer(5, 4, activation='relu')
lay1.W = W1
lay1.b = b1
lay2 = FCLayer(4, 3, activation='relu')
lay2.W = W2
lay2.b = b2
lay3 = FCLayer(3, 1, activation='sigmoid')
lay3.W = W3
lay3.b = b3
A = lay1.forward(X)
A = lay2.forward(A)
A = lay3.forward(A)
self.assertAlmostEqual(0.03921668, float(A[0, 0]), places=7)
self.assertAlmostEqual(0.19734387, float(A[0, 2]), places=7)