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refactor: separate gradient back and weight updates

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Lenoctambule
2026-04-12 19:40:04 +02:00
parent d23f3a903a
commit a4334568ec
2 changed files with 33 additions and 17 deletions
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17
src/easyvae/autoencoder.py
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@@ -82,9 +82,11 @@ class ClassicalAutoencoder(AAutoencoder):
self.encoder.forward(v)
)
error = out - v
self.encoder.backprop(
self.decoder.backprop(error)
self.encoder.back(
self.decoder.back(error)
)
self.encoder.backprop()
self.decoder.backprop()
return np.sum(np.abs(error)) / len(v)
@interruptable
@@ -109,7 +111,7 @@ class ClassicalAutoencoder(AAutoencoder):
error += self.train(x)
error /= len(data_set)
derror = prev_error - error
if derror <= 0 or abs(derror) < 1e-4:
if abs(derror) < 1e-4:
no_improv += 1
else:
no_improv = 0
@@ -167,11 +169,14 @@ class VariationalAutoencoder(AAutoencoder):
def train(self, v: np.ndarray) -> tuple[float, float]:
out, _ = self.forward(v)
error = out - v
self.encoder.backprop(
self.sampler.backprop(
self.decoder.backprop(error)
self.encoder.back(
self.sampler.back(
self.decoder.back(error)
)
)
self.encoder.backprop()
self.sampler.backprop()
self.decoder.backprop()
return np.mean(error ** 2), self.sampler.DKL()
@interruptable