def test_op_norm(self):
_ = Numlike().op_norm((3, 3, 3, 3), 1, 1, 1, 1)
def test_sum(self):
_ = Numlike().sum(0)
def test_T(self):
_ = Numlike().T
def test_max(self):
_ = Numlike().max(Numlike())
def test_shape(self):
_ = Numlike().reshape((1, 2, 3))
def test_exp(self):
_ = Numlike().exp()
def test_power(self):
_ = Numlike().power(3.0)
def test_op_d_conv(self):
_ = Numlike().op_d_conv((1, 1, 1, 1), (1, 1, 1), Numlike(), (1, 1),
(1, 1), 1)
def test_derest_output(self):
_ = Numlike.derest_output(3)
def test_op_d_avg_pool(self):
_ = Numlike().op_d_avg_pool(Numlike(), (2, 2, 2, 2), (3, 3), (3, 3),
(1, 1))
def test_op_d_norm(self):
_ = Numlike().op_d_norm(Numlike(), (1, 1, 1, 1), 1, 1, 1, 1)
def test_getitem(self):
_ = Numlike()[0]
def test_op_d_relu(self):
_ = Numlike().op_d_relu(Numlike())
def test_op_conv(self):
_ = Numlike().op_conv(Numlike(), (3, 3, 3), (3, 3, 3), Numlike(),
(1, 1), (2, 2), 1)
def test_reciprocal(self):
_ = Numlike().reciprocal()
def test_setitem(self):
Numlike()[0] = 0.0
def test_neg(self):
_ = Numlike().neg()
def test_shape(self):
_ = Numlike().shape()
def test_square(self):
_ = Numlike().square()
def test_add(self):
_ = Numlike() + Numlike()
def test_dot(self):
w = np.array([[1, 2], [3, 4]])
_ = Numlike().dot(w)
def test_sub(self):
_ = Numlike() - 1.0
def test_amax(self):
_ = Numlike().amax()
def test_mul(self):
_ = Numlike() * 3.0
def test_flatten(self):
_ = Numlike().flatten()
def test_div(self):
_ = Numlike() / 5.0
def test_abs(self):
_ = Numlike().abs()
def test_rdiv(self):
_ = 5.0 / Numlike()
def test_from_shape1(self):
_ = Numlike.from_shape((3, 4))
def test_op_softmax(self):
_ = Numlike().op_softmax(5)
