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lang-py
model.get_weights()after training the model, but the weights did not seem to be properly tied. The weights of the decoder did not seem to be the transpose of the decoder. I have not triedmodel.summary()yet, but that is a good call. I will update you when I test this. Thank you for the answer.model.get_weights()andmodel.summary(), but it did not seem like there were any indications that the weights were tied.self._trainable_weights.append(self.kernel)? These are not trainable weights of this layer but of the other. I think what happens is that they get updated in two places of the graph and that's why they are different.self._non_trainable_weights.append(self.kernel), but the weights still seem to be different. If the kernel is not added to either of these lists, it will not print when usingmodel.get_weights().model.get_weights()andmodel.summary()when you add to non-trainable weights. Also, could you share thecallmethod?