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feat: working implementation of VAE

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Lenoctambule
2026-04-05 01:17:51 +02:00
parent 577e679425
commit 5a8fb2c48b
3 changed files with 42 additions and 27 deletions
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29
autoencoder.py
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@@ -121,7 +121,6 @@ class VariationalAutoencoder(AAutoencoder):
def __init__(self,
encoder_layers: list[int],
decoder_layers: list[int],
sampling_size: int,
lr: float,
activation_func: ActivationFunc):
if encoder_layers[-1] != decoder_layers[0]:
@@ -131,7 +130,12 @@ class VariationalAutoencoder(AAutoencoder):
self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
self.sampler = SampleLayer(self.encoder.out_size, lr, activation_func)
self.sampling_size = sampling_size
def loss(self, data_set: list[np.ndarray]) -> float:
loss = 0
for x in data_set:
loss += np.sum(np.abs(x - self.forward(x)[0])) / len(x)
return loss / len(data_set)
def load(path: str) -> 'ClassicalAutoencoder':
path = path.removesuffix('.npy') + '.npy'
@@ -139,16 +143,17 @@ class VariationalAutoencoder(AAutoencoder):
return data.item()
def train(self, v: np.ndarray) -> float:
out_enc = self.encoder.forward(v)
in_samples = np.zeros(
(self.sampling_size, self.encoder.out_size)
out = self.forward(v)
error = out - v
self.encoder.backprop(
self.sampler.backprop(
self.decoder.backprop(error)
)
out_samples = np.zeros(
(self.sampling_size, self.decoder.out_size)
)
for i in range(self.sampling_size):
in_samples[i] = self.sampler.forward(out_enc)
out_samples[i] = self.decoder.forward(in_samples[i])
)
return np.sum(np.abs(error)) / len(v)
def forward(self, v: np.ndarray) -> np.ndarray:
pass
code = self.encoder.forward(v)
sample = self.sampler.forward(code)
out = self.decoder.forward(sample)
return out, code