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Refactor activation function and loss calculation (#325)

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AtomicVar 2024-01-17 05:42:56 +08:00 committed by GitHub
parent ce7b65e8c4
commit 2ba5d3db14
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2 changed files with 3 additions and 3 deletions
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4
mnist/main.py
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@ -26,12 +26,12 @@ class MLP(nn.Module):
def __call__(self, x): def __call__(self, x):
for l in self.layers[:-1]: for l in self.layers[:-1]:
x = mx.maximum(l(x), 0.0) x = nn.relu(l(x))
return self.layers[-1](x) return self.layers[-1](x)
def loss_fn(model, X, y): def loss_fn(model, X, y):
return mx.mean(nn.losses.cross_entropy(model(X), y)) return nn.losses.cross_entropy(model(X), y, reduction="mean")
def eval_fn(model, X, y): def eval_fn(model, X, y):

2
stable_diffusion/stable_diffusion/__init__.py
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@ -155,5 +155,5 @@ class StableDiffusion:
def decode(self, x_t): def decode(self, x_t):
x = self.autoencoder.decode(x_t) x = self.autoencoder.decode(x_t)
x = mx.minimum(1, mx.maximum(0, x / 2 + 0.5)) x = mx.clip(x / 2 + 0.5, 0, 1)
return x return x