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feat: save and load methods for Autoencoder

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Lenoctambule
2026-03-28 17:50:27 +01:00
parent 6155649655
commit efd328e530
3 changed files with 33 additions and 11 deletions
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31
mnist_test.py
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@@ -17,19 +17,17 @@ def load_mnist() -> list[np.ndarray]:
return res["x_train"], res["y_train"], res["x_test"], res["y_test"]
def mnist_test(
def mnist_train(
bottleneck: int,
max_epoch: int,
patience: int,
):
x_train, _, x_test, _ = load_mnist()
in_len = x_train[0].shape[0] * x_train[0].shape[0]
img_shape = x_train[0].shape
x_train.resize(x_train.shape[0], in_len)
x_test.resize(x_test.shape[0], in_len)
x_train = np.divide(x_train, 255)
x_test = np.divide(x_train, 255)
x_train = x_train[:1000]
x_train = x_train / 255
x_test = x_test / 255
autoencoder = Autoencoder(
[in_len, bottleneck],
[bottleneck, in_len],
@@ -41,9 +39,20 @@ def mnist_test(
max_epoch,
patience,
display_loss=True)
autoencoder.save("autoencoder_mnist")
def mnist_test():
x_train, _, x_test, _ = load_mnist()
in_len = x_train[0].shape[0] * x_train[0].shape[0]
img_shape = x_train[0].shape
x_train.resize(x_train.shape[0], in_len)
x_test.resize(x_test.shape[0], in_len)
x_train = x_train / 255
x_test = x_test / 255
autoencoder = Autoencoder.load('autoencoder_mnist.npy')
example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
code = autoencoder.encode(example.flatten())
output = autoencoder.decode(code)
output, _ = autoencoder.forward(example.flatten())
plt.subplot(1, 2, 1)
plt.matshow(example.reshape(img_shape), fignum=False)
plt.subplot(1, 2, 2)
@@ -55,10 +64,14 @@ if __name__ == "__main__":
import argparse
import sys
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('-r', action='store_true')
args = parser.parse_args(sys.argv[1:])
mnist_test(args.b, args.e, args.p)
if args.r:
mnist_test()
else:
mnist_train(args.b, args.e, args.p)
mnist_test()