def test_backward_dot_muti_array(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
a = rng.randn(*shape_a).astype(np.float32)
b = rng.randn(*shape_b).astype(np.float32)
inputs = [
a,
b,
]
function_network_tester(rng, F.dot, inputs)
def test_backward_dot_muti_array_out(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
a = rng.randn(*shape_a).astype(np.float32)
b = rng.randn(*shape_b).astype(np.float32)
inputs = [
a,
b,
]
v_a = nn.Variable.from_numpy_array(a)
v_b = nn.Variable.from_numpy_array(b)
ans = F.dot(v_a, v_b)
out = nn.Variable(ans.shape)
out.data.cast(np.float32)
function_network_tester(rng, F.dot, inputs, func_args=[out], args_out=True)
def test_backward_dot_have_scalar_out(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
inputs = []
func_args = []
if not shape_a:
a = rng.randn()
func_args += [a]
v_a = nn.Variable()
v_a.data.cast(np.float32)[...] = a
else:
a = rng.randn(*shape_a).astype(np.float32)
inputs += [a]
v_a = nn.Variable.from_numpy_array(a)
if not shape_b:
b = rng.randn()
func_args += [b]
v_b = nn.Variable()
v_b.data.cast(np.float32)[...] = b
else:
b = rng.randn(*shape_b).astype(np.float32)
inputs += [b]
v_b = nn.Variable.from_numpy_array(b)
ans = F.dot(v_a, v_b)
out = nn.Variable(ans.shape)
out.data.cast(np.float32)
func_args += [out]
function_network_tester(rng,
F.dot,
inputs,
func_args=func_args,
have_scalar=True,
args_out=True)
def test_backward_dot_have_scalar(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
inputs = []
func_args = []
if not shape_a:
a = rng.randn()
func_args += [a]
else:
a = rng.randn(*shape_a).astype(np.float32)
inputs += [a]
if not shape_b:
b = rng.randn()
func_args += [b]
else:
b = rng.randn(*shape_b).astype(np.float32)
inputs += [b]
function_network_tester(rng,
F.dot,
inputs,
func_args=func_args,
have_scalar=True)