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feat: NoiseLayer class + keep loss across trainings

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Lenoctambule
2026-04-12 20:31:31 +02:00
parent a4334568ec
commit 82acc6b4f3
2 changed files with 25 additions and 10 deletions
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24
src/easyvae/autoencoder.py
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@@ -1,6 +1,6 @@
import numpy as np
from tqdm import tqdm
from .layers import DeepNNLayer, SampleLayer
from .layers import DeepNNLayer, SampleLayer, NoiseLayer
from .activations import ActivationFunc, Identity
from .plotters import Plotter, CAPlotter, VAEPlotter
from .utils import interruptable
@@ -17,13 +17,15 @@ class AAutoencoder(ABC):
encoder_layers: list[int],
decoder_layers: list[int],
lr: float,
activation_func: ActivationFunc):
activation_func: ActivationFunc,
noise=0):
if encoder_layers[-1] != decoder_layers[0]:
raise Exception(
f"Encoder output and decoder input don't match {encoder_layers[-1]} != {encoder_layers[0]}" # noqa
)
self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
self.noise = NoiseLayer(noise)
self.space_dim = decoder_layers[0]
self.lr = lr
self.losses = [0]
@@ -78,8 +80,8 @@ class ClassicalAutoencoder(AAutoencoder):
return loss / len(data_set)
def train(self, v: np.ndarray):
out = self.decoder.forward(
self.encoder.forward(v)
out, _ = self.forward(
self.noise.forward(v)
)
error = out - v
self.encoder.back(
@@ -96,7 +98,8 @@ class ClassicalAutoencoder(AAutoencoder):
patience: int,
display_loss: bool = False) -> list[float]:
plotter = self.plotter_cls(self) if display_loss else Plotter(self)
self.losses = [self.loss(data_set)]
if len(self.losses) == 0:
self.losses = [self.loss(data_set)]
epoch = 0
no_improv = 0
prev_error = self.losses[0]
@@ -167,7 +170,9 @@ class VariationalAutoencoder(AAutoencoder):
return recon_loss, kl_loss
def train(self, v: np.ndarray) -> tuple[float, float]:
out, _ = self.forward(v)
out, _ = self.forward(
self.noise.forward(v)
)
error = out - v
self.encoder.back(
self.sampler.back(
@@ -186,9 +191,10 @@ class VariationalAutoencoder(AAutoencoder):
patience: int,
display_loss: bool = False) -> list[float]:
plotter = self.plotter_cls(self) if display_loss else Plotter(self)
recon_0, kl_0 = self.loss(data_set)
self.recon_losses = [recon_0]
self.KL_losses = [kl_0]
if len(self.recon_losses) == 0:
recon_0, kl_0 = self.loss(data_set)
self.recon_losses = [recon_0]
self.KL_losses = [kl_0]
epoch = 0
no_improv = 0
prev_loss = self.recon_losses[0] + self.KL_losses[0]