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feat: move train over dataset logic to Autoencoder class

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Lenoctambule
2026-03-27 07:07:41 +01:00
parent af9a0c70b2
commit 9859863ec9
2 changed files with 56 additions and 39 deletions
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45
autoencoder.py
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@@ -1,7 +1,12 @@
import numpy as np
from utils import regularize
from utils import (regularize,
dynamic_loss_plot_init,
dynamic_loss_plot_update,
dynamic_loss_plot_finish)
import types
LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
class Encoder:
def __init__(self,
@@ -73,6 +78,44 @@ class Autoencoder:
error = v - reconstructed
return np.sum(np.abs(error))
def train_dataset(self,
data_set: list[np.ndarray],
max_epoch: int,
patience: int,
display_loss: bool = False) -> list[float]:
if display_loss is True:
ax, line = dynamic_loss_plot_init()
losses = []
epoch = 0
no_improv = 0
prev_error = float('inf')
while True:
print(
f"{LOADER[epoch % len(LOADER)]} Training \t({epoch=} error={prev_error:.2f})", # noqa
end="\r"
)
error = 0
for x in data_set:
input = x.flatten()
error += self.train(input)
error /= len(data_set)
if error - prev_error <= 1e-8:
no_improv += 1
else:
no_improv = 0
prev_error = float(error)
losses.append(error)
if display_loss is True:
dynamic_loss_plot_update(ax, line, losses)
if no_improv > patience:
break
if epoch > max_epoch:
break
epoch += 1
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)

50
mnist_test.py
View File

@@ -2,48 +2,23 @@ import matplotlib.pyplot as plt
import numpy as np
import keras
from autoencoder import Autoencoder
from utils import (relu,
dynamic_loss_plot_init,
dynamic_loss_plot_update,
dynamic_loss_plot_finish)
from utils import relu
def mnist_embed(
def mnist_test(
bottleneck: int,
max_epoch: int,
patience: int,
):
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
x_train = np.divide(x_train, 255)
x_test = np.divide(x_train, 255)
in_len = x_train[0].flatten().shape[0]
autoencoder = Autoencoder(in_len, bottleneck, 0.001, relu)
ax, line = dynamic_loss_plot_init()
no_improv = 0
prev_error = float('inf')
losses = []
epoch = 0
autoencoder = Autoencoder(in_len, bottleneck, 0.0001, relu)
x_train = x_train[:]
while True:
error = 0
for x in x_train:
input = x.flatten() / 255
error += autoencoder.train(input)
error /= len(x_train)
if error - prev_error <= 1e-8:
no_improv += 1
else:
no_improv = 0
prev_error = error
losses.append(error)
dynamic_loss_plot_update(ax, line, losses)
if no_improv > patience:
break
if epoch > max_epoch:
break
epoch += 1
print("Done!")
dynamic_loss_plot_finish(ax, line)
autoencoder.train_dataset(x_train, max_epoch, patience)
example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
code = autoencoder.encode(example.flatten() / 255)
code = autoencoder.encode(example.flatten())
output = autoencoder.decode(code)
plt.subplot(1, 2, 1)
plt.matshow(example, fignum=False)
@@ -58,9 +33,8 @@ if __name__ == "__main__":
options = "b:e:p:"
parser = argparse.ArgumentParser()
parser.add_argument('-b', type=int, nargs='+', default=50)
parser.add_argument('-e', type=int, nargs='+', default=1000)
parser.add_argument('-p', type=int, nargs='+', default=5)
parser.add_argument('-b', type=int, nargs='?', default=50)
parser.add_argument('-e', type=int, nargs='?', default=1000)
parser.add_argument('-p', type=int, nargs='?', default=5)
args = parser.parse_args(sys.argv[1:])
mnist_embed(args.b, args.e, args.p)
mnist_test(args.b, args.e, args.p)