micrograd

Implementation of Karpathy's micrograd

x = [2.0, 3.0, -1.0]
n = MLP(3, [4, 4, 1])
n(x)

xs = [
    [2.0, 3.0, -1.0],
    [3.0, -1.0, 0.6],
    [6.0, 1.0, 5.0],
    [1.0, 3.0, 2.0]
]
ys = [1.0, -1.0, 1.0, -1.0]

for k in range(20):
    #forward pass
    ypred = [n(x) for x in xs]
    loss = sum([(ygt - yout) ** 2 for ygt, yout in zip(ys, ypred)])

    # zero grad
    for p in n.parameters():
        p.grad = 0
    
    #backward pass
    loss.backward()

    #update
    for p in n.parameters():
        p.data += -0.05 * p.grad
    
    print(k, loss.data)

pawelropa/micrograd

micrograd