from tinygrad.tensor import Tensor
import tinygrad.optim as optim
from tinygrad.utils import layer_init_uniform
 
 
class TinyBobNet:
  def __init__(self):
    self.l1 = Tensor(layer_init_uniform(784, 128))
    self.l2 = Tensor(layer_init_uniform(128, 10))
 
 
  def forward(self, x):
    return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
 
 
model = TinyBobNet()
optim = optim.SGD([model.l1, model.l2], lr=0.001)
 
 
# ... and complete like pytorch, with (x,y) data
 
 
out = model.forward(x)
loss = out.mul(y).mean()
loss.backward()
optim.step()

from tinygrad.tensor import Tensor(Tensor.ones(4,4).cuda() + Tensor.ones(4,4).cuda()).cpu()