nairouz / dynae Goto Github PK

Hi,

it appears to me there is an issue during training in the published version of the code leading to the critic/discriminator weights not being trained. In function aci_ae_constructor in file DynAE.py the weights of the critic are set as non-trainable, because the critic should not be trained when the auto encoder is trained: critic.trainable = False. However, this change also propagates to the critic and discriminator model, such that DynAE.train_on_batch_disc() does not update any weights.

This can be verified in two ways, either checking whether the critic weights are updated by inserting into DynAE.py line 333 following:

        last_critic_weights = None
        
        #Training loop
        for ite in range(int(maxiter)):
            #Validation interval
            if ite % validate_interval == 0:
                # Check weights modified
                critic_weights = self.disc.get_weights()
                if last_critic_weights is not None:
                    if all([np.all(critic_weights[i] == last_critic_weights[i]) for i in range(len(critic_weights))]):
                        print("Critic weights were not modified during training")
                last_critic_weights = [np.copy(c) for c in critic_weights]

or by directly checking whether the discriminator model has any trainable weights, i.e. modifying:

    def train_on_batch_disc(self, x1, x2, y1, y2):
        y = np.zeros((x1.shape[0],))
        if len(self.disc.trainable_weights) == 0:
            print("The discriminator is being trained with no trainable weights")
        return self.disc.train_on_batch([x1, x2, y1, y2], y)

It is suprising to me how well the model performs despite the discriminator not being trained, but it seems to me that your published work does things differently. Is this a mistake?

Kind regards

Can you guide me as to how you have plotted the embeddings, as visualizing 2D embedded subspaces using TSNE is showing errors? Are you using the npy files generated in the embeddings?