def acc(w1, b1, w2, b2, x, t):
accuracy_cnt = 0
affine1 = Affine(w1, b1)
affine2 = Affine(w2, b2)
sigmoid = Sigmoid()
x1 = affine1.forward(x)
y1 = sigmoid.forward(x1)
x2 = affine2.forward(y1)
for i in range(len(t)):
if abs(x2[i][0] - t[i][0]) < 0.5:
accuracy_cnt += 1
#print(x2[i][0],t[i][0])
print(accuracy_cnt / 64)
class TestAffine(unittest.TestCase):
def setUp(self):
W = np.random.randn(2, 4)
b = np.random.randn(4)
self.affine = Affine(W, b)
def test_forward(self):
out = self.affine.forward(np.random.randn(4, 2))
self.assertEqual((4, 4), out.shape)
def test_backward(self):
self.affine.forward(np.random.randn(4, 2))
dx = self.affine.backward(np.random.randn(4, 4))
self.assertEqual((4, 2), dx.shape)
def main():
train_data = np.array([[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5],
[0, 6], [0, 7], [1, 0], [1, 1], [1, 2], [1, 3],
[1, 4], [1, 5], [1, 6], [1, 7], [2, 0], [2, 1],
[2, 2], [2, 3], [2, 4], [2, 5], [2, 6], [2, 7],
[3, 0], [3, 1], [3, 2], [3, 3], [3, 4], [3, 5],
[3, 6], [3, 7], [4, 0], [4, 1], [4, 2], [4, 3],
[4, 4], [4, 5], [4, 6], [4, 7], [5, 0], [5, 1],
[5, 2], [5, 3], [5, 4], [5, 5], [5, 6], [5, 7],
[6, 0], [6, 1], [6, 2], [6, 3], [6, 4], [6, 5],
[6, 6], [6, 7], [7, 0], [7, 1], [7, 2], [7, 3],
[7, 4], [7, 5], [7, 6], [7, 7]])
label_data = np.array(
[[0], [1], [2], [3], [4], [5], [6], [7], [1], [0], [3], [2], [5], [4],
[7], [6], [2], [3], [0], [1], [6], [7], [4], [5], [3], [2], [1], [0],
[7], [6], [5], [4], [4], [5], [6], [7], [0], [1], [2], [3], [5], [4],
[7], [6], [1], [0], [3], [2], [6], [7], [4], [5], [2], [3], [0], [1],
[7], [6], [5], [4], [3], [2], [1], [0]])
learn_rate = 0.15
epoch = 600000
loss_list = []
w1, b1, w2, b2 = init(input_size=2, hidden_size=16, output_size=1)
#print(w1, w2, b1, b2)
for i in range(epoch):
affine1 = Affine(w1, b1)
affine2 = Affine(w2, b2)
sigmoid = Sigmoid()
loss = MSE()
x1 = affine1.forward(train_data)
y1 = sigmoid.forward(x1)
x2 = affine2.forward(y1)
ls = loss.mean_square_error(x2, label_data)
print(ls)
loss_list.append(ls)
dout = loss.backward(x2, label_data)
dx = affine2.backward(dout)
w2 = w2 - learn_rate * affine2.dw
b2 = b2 - learn_rate * affine2.db
dy1 = sigmoid.backward(dx)
dx = affine1.backward(dy1)
b1 = b1 - learn_rate * affine1.db
w1 = w1 - learn_rate * affine1.dw
#print(w1,w2,b1,b2)
plt.plot(loss_list)
plt.show()
acc(w1, b1, w2, b2, train_data, label_data)
class TestAffine(unittest.TestCase):
def setUp(self):
W = np.array([[-0.22472106, -0.42868683, 0.21713442],
[-0.13635294, 0.45327181, -1.31839392]])
b = np.array([1.55270156, 1.44441689, -1.69451485])
self.affine = Affine(W, b)
def test_forward(self):
x = np.array([1.52949391, -0.81788271])
assert_almost_equal(([1.32051278, 0.41801982, -0.28411748]),
self.affine.forward(x))
def test_backward(self):
x = np.array([1.52949391, -0.81788271])
self.affine.forward(x)
dout = 1
assert_almost_equal(
np.array([[-0.2247211, -0.1363529], [-0.4286868, 0.4532718],
[0.2171344, -1.3183939]]), self.affine.backward(dout))
