lenoctambule/autoencoder
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: use DeepNNLayer in Autoencoder

Browse Source
This commit is contained in:
Lenoctambule
2026-03-28 02:07:23 +01:00
parent a50a09b337
commit e5520bf050
2 changed files with 28 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
autoencoder.py
View File

@@ -3,27 +3,28 @@ from utils import (dynamic_loss_plot_init,
dynamic_loss_plot_update,
dynamic_loss_plot_finish)
from tqdm import tqdm
from layers import NNLayer
from layers import DeepNNLayer
LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
class Autoencoder:
def __init__(self,
in_len: int,
bottleneck: int,
encoder_layers: list[int],
decoder_layers: list[int],
lr: float,
activation_func):
self.encoder = NNLayer(in_len, bottleneck, lr, activation_func)
self.decoder = NNLayer(bottleneck, in_len, lr, activation_func)
self.encoder = DeepNNLayer(encoder_layers, lr, activation_func)
self.decoder = DeepNNLayer(decoder_layers, lr, activation_func)
def train(self, v: np.ndarray) -> float:
encoded = self.encoder.forward(v)
reconstructed = self.decoder.forward(encoded)
error = self.decoder.backprop(reconstructed - v)
self.encoder.backprop(error)
error = v - reconstructed
return np.sum(np.abs(error))
def train(self, v: np.ndarray):
out = self.decoder.forward(
self.encoder.forward(v)
)
self.encoder.backprop(
self.decoder.backprop(out - v)
)
return np.sum(np.abs(out - v)) / len(v)
def train_dataset(self,
data_set: list[np.ndarray],
@@ -60,10 +61,10 @@ class Autoencoder:
if epoch > max_epoch:
break
epoch += 1
if display_loss is True:
dynamic_loss_plot_finish(ax, line)
print("#Training complete !")
return losses
print("Training complete !")
if display_loss is True:
dynamic_loss_plot_finish(ax, line)
return losses
def encode(self, v: np.ndarray) -> np.ndarray:
return self.encoder.forward(v)

13
mnist_test.py
View File

@@ -26,8 +26,17 @@ def mnist_test(
x_train = np.divide(x_train, 255)
x_test = np.divide(x_train, 255)
in_len = x_train[0].shape[0] * x_train[0].shape[0]
autoencoder = Autoencoder(in_len, bottleneck, 0.001, relu)
autoencoder.train_dataset(x_train, max_epoch, patience, display_loss=True)
autoencoder = Autoencoder(
[in_len, bottleneck],
[bottleneck, in_len],
0.1,
relu
)
autoencoder.train_dataset(
x_train,
max_epoch,
patience,
display_loss=True)
example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
code = autoencoder.encode(example.flatten())
output = autoencoder.decode(code)