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Smaller MicroGrad

Tried to do autograd engine based on Karpathy's micrograd repo (and on the basis of PyTorch)

Examples

Regression (it actually learns something!)

from autograd.tensor import Tensor
from autograd import nn
from autograd.optim import SGD
from sklearn.datasets import load_boston


class Regressor(nn.Module):
    def __init__(self,
                 in_features: int) -> None:
        super().__init__()

        self.l1 = nn.Linear(in_features, 32)
        self.l2 = nn.Linear(32, 1)
    
    def forward(self, x):
        return self.l2(self.l1(x).relu())


x, y = load_boston(return_X_y=True)
x, y = Tensor(x), Tensor(y).reshape(-1, 1)


net = Regressor(x.shape[1])
optimizer = SGD(net.parameters(), lr=1e-6)

errors = []
iters = 1000

for i in range(iters):
    net.zero_grad()

    loss = nn.MSELoss(net(x), y)
    errors.append(loss.data.flatten())

    loss.backward()
    optimizer.step()

print(np.sqrt(loss.data[0]))

Classification (it also learns something!)

from autograd.tensor import Tensor
from autograd import nn
from autograd.optim import SGD
from sklearn.datasets import load_iris


class Classifier(nn.Module):
    def __init__(self,
                 in_features: int,
                 out_features: int) -> None:
        super().__init__()

        self.l1 = nn.Linear(in_features, 32)
        self.l2 = nn.Linear(32, out_features)
    
    def forward(self, x):
        out = self.l1(x).relu()
        return self.l2(out).softmax(axis=1)


x, y = load_iris(return_X_y=True)
x = (x - x.mean(axis=0)) / x.std(axis=0)
x, y = Tensor(x), nn.one_hot(y)


net = Classifier(in_features=x.shape[1], out_features=3)
optimizer = SGD(net.parameters(), lr=3e-4)

errors = []
iters = 10000

for i in range(iters):
    net.zero_grad()

    loss = nn.CrossEntropyLoss(net(x), y)
    errors.append(loss.data.flatten())

    loss.backward()
    optimizer.step()

print(loss)

References