The current inference section appears to contain a conceptual error regarding the use of the autoencoder output.
Current code:
encoded_data = model.predict(x_test)
decoded_data = model.predict(encoded_data)
Issue:
model.predict(x_test) already returns the reconstructed images produced by the autoencoder. Therefore, the variable encoded_data does not contain encoded latent vectors; it actually contains decoded/reconstructed images.
As a result, the following line:
decoded_data = model.predict(encoded_data)
feeds reconstructed images back into the autoencoder a second time, which is conceptually incorrect and may lead to misleading results.
Correct approach:
decoded_data = model.predict(x_test)
The current inference section appears to contain a conceptual error regarding the use of the autoencoder output.
Current code:
Issue:
model.predict(x_test) already returns the reconstructed images produced by the autoencoder. Therefore, the variable encoded_data does not contain encoded latent vectors; it actually contains decoded/reconstructed images.
As a result, the following line:
decoded_data = model.predict(encoded_data)feeds reconstructed images back into the autoencoder a second time, which is conceptually incorrect and may lead to misleading results.
Correct approach:
decoded_data = model.predict(x_test)