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.
56 lines
1.9 KiB
56 lines
1.9 KiB
1 month ago
|
from tinygrad.helpers import trange
|
||
|
|
from tinygrad.nn.datasets import mnist
|
||
|
|
import mlx.core as mx
|
||
|
|
import mlx.nn as nn
|
||
|
|
import mlx.optimizers as optim
|
||
|
|
from functools import partial
|
||
|
|
|
||
|
|
class Model(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
self.c1 = nn.Conv2d(1, 32, 5)
|
||
|
|
self.c2 = nn.Conv2d(32, 32, 5)
|
||
|
|
self.bn1 = nn.BatchNorm(32)
|
||
|
|
self.m1 = nn.MaxPool2d(2)
|
||
|
|
self.c3 = nn.Conv2d(32, 64, 3)
|
||
|
|
self.c4 = nn.Conv2d(64, 64, 3)
|
||
|
|
self.bn2 = nn.BatchNorm(64)
|
||
|
|
self.m2 = nn.MaxPool2d(2)
|
||
|
|
self.lin = nn.Linear(576, 10)
|
||
|
|
def __call__(self, x):
|
||
|
|
x = mx.maximum(self.c1(x), 0)
|
||
|
|
x = mx.maximum(self.c2(x), 0)
|
||
|
|
x = self.m1(self.bn1(x))
|
||
|
|
x = mx.maximum(self.c3(x), 0)
|
||
|
|
x = mx.maximum(self.c4(x), 0)
|
||
|
|
x = self.m2(self.bn2(x))
|
||
|
|
return self.lin(mx.flatten(x, 1))
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
X_train, Y_train, X_test, Y_test = mnist()
|
||
|
|
X_train = mx.array(X_train.float().permute((0,2,3,1)).numpy())
|
||
|
|
Y_train = mx.array(Y_train.numpy())
|
||
|
|
X_test = mx.array(X_test.float().permute((0,2,3,1)).numpy())
|
||
|
|
Y_test = mx.array(Y_test.numpy())
|
||
|
|
|
||
|
|
model = Model()
|
||
|
|
optimizer = optim.Adam(1e-3)
|
||
|
|
def loss_fn(model, x, y): return nn.losses.cross_entropy(model(x), y).mean()
|
||
|
|
|
||
|
|
state = [model.state, optimizer.state]
|
||
|
|
@partial(mx.compile, inputs=state, outputs=state)
|
||
|
|
def step(samples):
|
||
|
|
# Compiled functions will also treat any inputs not in the parameter list as constants.
|
||
|
|
X,Y = X_train[samples], Y_train[samples]
|
||
|
|
loss_and_grad_fn = nn.value_and_grad(model, loss_fn)
|
||
|
|
loss, grads = loss_and_grad_fn(model, X, Y)
|
||
|
|
optimizer.update(model, grads)
|
||
|
|
return loss
|
||
|
|
|
||
|
|
test_acc = float('nan')
|
||
|
|
for i in (t:=trange(70)):
|
||
|
|
samples = mx.random.randint(0, X_train.shape[0], (512,)) # putting this in JIT didn't work well
|
||
|
|
loss = step(samples)
|
||
|
|
if i%10 == 9: test_acc = ((model(X_test).argmax(axis=-1) == Y_test).sum() * 100 / X_test.shape[0]).item()
|
||
|
|
t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
|