def test_max_ops():
t1 = Tensor([[1, 3, 5], [3, 7, -2]], requires_grad=True)
t2 = ops.max(t1, axis=None)
t3 = ops.max(t1, axis=0)
assert t2.values == 7
assert t3.values.tolist() == [3, 7, 5]
t2.backward()
assert t1.grad.tolist() == [[0, 0, 0], [0, 1, 0]]
t1.zero_grad()
t3.backward([1, 1, 1])
assert t1.grad.tolist() == [[0, 0, 1], [1, 1, 0]]
def test_minimal_nn():
x = Tensor(np.random.normal(0, 1.0, (100, 3)))
y = x * 3.14 + 30
w1 = Tensor(np.random.normal(0, 1.0, (3, 3)), requires_grad=True)
b1 = Tensor(np.random.normal(0, 1.0, 3), requires_grad=True)
previous_loss = 1e10
for _ in range(100):
w1.zero_grad()
b1.zero_grad()
predicted = x @ w1 + b1
err = predicted - y
loss = (err**2).sum()
loss.backward()
w1 -= 0.001 * w1.grad
b1 -= 0.001 * b1.grad
assert loss.values < previous_loss
previous_loss = loss.values