You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
47 lines
1.9 KiB
47 lines
1.9 KiB
# model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
|
|
from typing import Callable
|
|
from tinygrad import Tensor, TinyJit, nn, GlobalCounters
|
|
from tinygrad.helpers import getenv, colored, trange
|
|
from tinygrad.nn.datasets import mnist
|
|
|
|
class Model:
|
|
def __init__(self):
|
|
self.layers: list[Callable[[Tensor], Tensor]] = [
|
|
nn.Conv2d(1, 32, 5), Tensor.relu,
|
|
nn.Conv2d(32, 32, 5), Tensor.relu,
|
|
nn.BatchNorm(32), Tensor.max_pool2d,
|
|
nn.Conv2d(32, 64, 3), Tensor.relu,
|
|
nn.Conv2d(64, 64, 3), Tensor.relu,
|
|
nn.BatchNorm(64), Tensor.max_pool2d,
|
|
lambda x: x.flatten(1), nn.Linear(576, 10)]
|
|
|
|
def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
|
|
|
|
if __name__ == "__main__":
|
|
X_train, Y_train, X_test, Y_test = mnist(fashion=getenv("FASHION"))
|
|
|
|
model = Model()
|
|
opt = (nn.optim.Adam if not getenv("MUON") else nn.optim.Muon)(nn.state.get_parameters(model))
|
|
|
|
@TinyJit
|
|
@Tensor.train()
|
|
def train_step() -> Tensor:
|
|
opt.zero_grad()
|
|
samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
|
|
loss = model(X_train[samples]).sparse_categorical_crossentropy(Y_train[samples]).backward()
|
|
return loss.realize(*opt.schedule_step())
|
|
|
|
@TinyJit
|
|
def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
|
|
|
|
test_acc = float('nan')
|
|
for i in (t:=trange(getenv("STEPS", 70))):
|
|
GlobalCounters.reset() # NOTE: this makes it nice for DEBUG=2 timing
|
|
loss = train_step()
|
|
if i%10 == 9: test_acc = get_test_acc().item()
|
|
t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
|
|
|
|
# verify eval acc
|
|
if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
|
|
if test_acc >= target and test_acc != 100.0: print(colored(f"{test_acc=} >= {target}", "green"))
|
|
else: raise ValueError(colored(f"{test_acc=} < {target}", "red"))
|
|
|