print('[Answer]')
print(correct_sm)
print('[Submit]')
print(sm)
print()
y = np.array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.]])
y_hat = np.array([[0.25, 0.25, 0.25, 0.25], [0.9, 0.033, 0.033, 0.033],
[0.0, 0.0, 1.0, 0.0]])
# Cross-entropy Loss
# Backward
# For d_softmax, difference should be very small a
ce_loss = softmax_layer.ce_loss(y_hat, y)
d_softmax = softmax_layer.backward(d_prev=1)
print('CE Loss Difference : ', correct_ce_loss - ce_loss)
print()
print(' d_softmax ')
print('[Answer]')
print(correct_d_softmax)
print('[Submit]')
print(d_softmax)
print()
# ===========================================================================
print(
'============================= 7. CNN Classifier ==========================='
class TestSoftmaxLayer(unittest.TestCase):
def setUp(self):
self.softmax_layer = SoftmaxLayer()
def test_softmax_layer_1_forward(self):
print('\n==================================')
print(' Test softmax layer forward ')
print('==================================')
np.random.seed(123)
x = np.random.randn(5, 5)
softmax_out = self.softmax_layer.forward(x)
correct_out = [
[0.06546572, 0.52558012, 0.25727246, 0.04298548, 0.10869621],
[0.52561239, 0.00890353, 0.06564194, 0.3574744, 0.04236773],
[0.07215113, 0.12940411, 0.63209441, 0.07509449, 0.09125586],
[0.02836542, 0.39760227, 0.39006297, 0.11953103, 0.06443832],
[0.20005518, 0.42494369, 0.03753939, 0.31014926, 0.02731249]
]
e = rel_error(correct_out, softmax_out)
print('Relative difference:', e)
self.assertTrue(e <= 1e-6)
out_sum = np.sum(softmax_out)
sum_e = out_sum - len(x)
print('Softmax sum error:', sum_e)
self.assertTrue(sum_e == 0)
def test_softmax_layer_2_ce_loss(self):
print('\n==================================')
print(' Test softmax layer ce loss ')
print('==================================')
np.random.seed(123)
x = np.random.randn(5, 5)
num_data, num_classes = x.shape
y_hat = self.softmax_layer.forward(x)
y = np.zeros_like(y_hat)
y_labels = np.random.permutation(num_data)
y[list(range(len(y_labels))), y_labels] = 1
loss = self.softmax_layer.ce_loss(y_hat, y)
correct_loss = 2.3052757961131616
e = rel_error(correct_loss, loss)
print('Relative difference:', e)
self.assertTrue(e <= 1e-11)
def test_softmax_layer_3_backward(self):
print('\n==================================')
print(' Test softmax layer backward ')
print('==================================')
np.random.seed(123)
x = np.random.randn(5, 5)
num_data, num_classes = x.shape
y_hat = self.softmax_layer.forward(x)
y = np.zeros_like(y_hat)
y_labels = np.random.permutation(num_data)
y[list(range(len(y_labels))), y_labels] = 1
loss = self.softmax_layer.ce_loss(y_hat, y)
dx = self.softmax_layer.backward(d_prev=1)
correct_dx = [
[0.01309314, 0.10511602, -0.14854551, 0.0085971, 0.02173924],
[0.10512248, 0.00178071, 0.01312839, 0.07149488, -0.19152645],
[0.01443023, 0.02588082, 0.12641888, -0.1849811, 0.01825117],
[-0.19432692, 0.07952045, 0.07801259, 0.02390621, 0.01288766],
[0.04001104, -0.11501126, 0.00750788, 0.06202985, 0.0054625]
]
e = rel_error(correct_dx, dx)
print('Relative difference:', e)
self.assertTrue(e <= 1e-6)
def runTest(self):
self.test_softmax_layer_1_forward()
self.test_softmax_layer_2_ce_loss()
self.test_softmax_layer_3_backward()
