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feat: loss method + mv data reshaping out of Autoencoder class

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Lenoctambule
2026-03-28 02:40:10 +01:00
parent e5520bf050
commit 6155649655
3 changed files with 25 additions and 11 deletions
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20
autoencoder.py
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@@ -17,6 +17,12 @@ class Autoencoder:
self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
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 train(self, v: np.ndarray):
out = self.decoder.forward(
self.encoder.forward(v)
@@ -31,12 +37,12 @@ class Autoencoder:
max_epoch: int,
patience: int,
display_loss: bool = False) -> list[float]:
losses = [self.loss(data_set)]
if display_loss is True:
ax, line = dynamic_loss_plot_init()
losses = []
ax, line = dynamic_loss_plot_init(losses)
epoch = 0
no_improv = 0
prev_error = float('inf')
prev_error = losses[0]
with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar:
while True:
lbar.set_description(
@@ -45,8 +51,7 @@ class Autoencoder:
lbar.update()
error = 0
for x in data_set:
input = x.flatten()
error += self.train(input)
error += self.train(x)
error /= len(data_set)
if prev_error - error <= 1e-8:
no_improv += 1
@@ -71,3 +76,8 @@ class Autoencoder:
def decode(self, v: np.ndarray) -> np.ndarray:
return self.decoder.forward(v)
def forward(self, v: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
code = self.encode(v)
out = self.decode(code)
return out, code