def test_tensor_mul():
a = 0.5 * sn.ones((3, 4), requires_grad=True)
a = a * 2.0
b = sn.random((3, 1))
c = b * a
d = sn.random((1, 4))
d = d * c
e = d.sum()
e.backward()
def test_tensor_div():
a = 0.5 / sn.ones((3, 4), requires_grad=True)
a = a / 2.0
b = sn.random((3, 1))
c = b / a
d = sn.random((1, 4))
d = d / c
e = d.sum()
e.backward()
def test_cross_entropy_layer():
x = sn.random((3, 4), requires_grad=True)
y = sn.zeros((3, 4))
y.set_values(np.array([[0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0]]))
c = CrossEntropyLayer()
loss = c(x, y)
loss.backward()
def test_tensor_sub():
a = sn.zeros((3, 4), requires_grad=True)
a = a - 1.0
a = 2.0 - a
b = sn.ones((3, 1))
b = b - a
c = sn.ones((1, 4))
c = c - b
d = sn.random((3, 4))
d = d - c
e = d.sum()
e.backward()
def test_tensor_add():
a = sn.zeros((3, 4), requires_grad=True)
a = a + 1.0
a = 2.0 + a
b = sn.ones((3, 1))
b = b + a
c = sn.ones((1, 4))
c = c + b
d = sn.random((3, 4))
d = d + c
e = d.sum()
e.backward()
def test_run_on_gpu():
x = sn.random((3, 4), device="cuda", requires_grad=True)
y = x * 2 + 1
z = sn.sum(y)
z = z * 2
z.backward()
def __init__(self, features, samples):
self.initial_x = sn.random((samples, features), requires_grad=False)
self.initial_y = sn.random((samples, 1), requires_grad=False)
def test_mse_layer():
x = sn.random((10, 3), requires_grad=True)
y = sn.random((10, 3))
mse = MSELayer()
loss = mse(x, y)
loss.backward()
def test_relu_layer():
r = SigmoidLayer()
x = sn.random((3, 4), requires_grad=True)
x = r(x)
loss = x.sum()
loss.backward()
def test_tanh_layer():
t = TanhLayer()
x = sn.random((3, 4), requires_grad=True)
x = t(x)
loss = x.sum()
loss.backward()
def test_sigmoid_layer():
s = SigmoidLayer()
x = sn.random((3, 4), requires_grad=True)
x = s(x)
loss = x.sum()
loss.backward()
def test_linear_layer():
l1 = LinearLayer(input_nodes=3, output_nodes=1)
x = sn.random((1, 3))
x = l1(x)
loss = x.sum()
loss.backward()
def test_tensor_cross_entropy():
a = sn.random((3, 4), requires_grad=True)
b = sn.zeros((3, 4))
b.set_values(np.array([[0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0]]))
c = sn.cross_entropy(a, b)
c.backward()
def test_tensor_mse():
a = sn.random((3, 4), requires_grad=True)
b = sn.random((3, 4), requires_grad=True)
c = sn.mse(a, b)
c.backward()
def test_tensor_relu():
a = sn.random((3, 4), requires_grad=True)
b = sn.relu(a)
b.set_retain_grad()
c = b.sum()
c.backward()
