for i in range(1, 10):
xs.append(Tensor([[i]]))
ys.append(Tensor([[i * 2]]))
w = Tensor([[.1]], requires_grad=True)
b = Tensor([.0], requires_grad=True)
for i in range(9):
for j in range(9):
x = xs[j]
y = ys[j]
print('x:')
print(x.narray)
print('y:')
print(y.narray)
y_ = x.matmul(w).add(b)
print('y_:')
print(y_.narray)
loss = y_.sub(y).pow(Tensor(2.))
print('loss:')
print(loss.narray)
loss.backward()
# print(w.grad)
# print(b.grad)
w.narray = w.narray - (0.01 * w.grad.T)
b.narray = b.narray - (0.01 * b.grad.T)
print(w.narray)
print(b.narray)
from autograd import Tensor x = Tensor([[1., 2., 3.]]) w = Tensor([[2.], [3.], [4.]], requires_grad=True) b = Tensor([.0], requires_grad=True) y_ = x.matmul(w).add(b) y = Tensor([60.]) loss = y_.sub(y).pow(Tensor(2.)).div(Tensor(2.)) print(loss.narray) loss.backward() print(w.grad) print(b.grad) w.narray = w.narray + (0.001 * w.grad.T) b.narray = b.narray + (0.001 * b.grad.T) print(w.narray) print(b.narray)