def test_l1_regularizer09():
scale = 0.5
weights = Tensor([[False, False], [False, False]])
try:
net = nn.L1Regularizer(scale)
net(weights)
except TypeError:
assert True
def test_l1_regularizer08():
scale = 0.5
net = nn.L1Regularizer(scale)
weights = Tensor(np.array([[1.0, -2.0], [-3.0, 4.0]]).astype(np.float32))
output = net(weights)
expect = 5.0
print("output : ", output.asnumpy())
assert np.all(output.asnumpy() == expect)
def test_l1_regularizer_input_tuple():
scale = 0.5
net = nn.L1Regularizer(scale)
weights = (1, 2, 3, 4)
try:
output = net(weights)
print("output : ", output.asnumpy())
except TypeError:
assert True
def __init__(self, scale):
super(Net_l1_regularizer, self).__init__()
self.l1_regularizer = nn.L1Regularizer(scale)
