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@mazatov ``` if(flag_multi_class): img = img / 255 mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0] new_mask=np.reshape(mask,[mask.shape[0],mask.shape[1]*mask.shape[2]]) new_mask = to_categorical(new_mask, num_classes=num_class, dtype='uint8') mask = new_mask ``` Hello, I used the...

@isayev My SMILES contains extra characters `a`, because the characters contain `Na`, `Ca`, what do you mean by standardized SMILES? What do I need to do? Thank you

@gmseabra Hello, I didn't see what you mean, but, as far as I know, your generation is based on reinforcement learning strategies. For example, the rules you set are high...

@isayev So what are the rules for intensive learning? The paper actually mentions that the new molecules generated by the generator are under the conditions of intensive learning. Now I...

@isayev Hello, I mean, I want to use my SMILES data to train a build model for generating new molecules, but I hope that the generated model does not generate...

@isayev Thank you for your patience. Sorry, I may not have described it in great detail. The method you said should be migration learning. I will use the migration learning...

@isayev So can I use the generator to generate new molecules directly? I don't care to reinforce the rules in learning? If so, thank you very much, I will try...

@isayev I suddenly thought of another problem. My build model should be used together with the predictive model. This should use the `GAN` method. If no predictive model continuously feeds...

@gmseabra Ok, thank you very much for your patience, this may be what I want, I will try it, thank you very much! Best!

@gmseabra Sorry, I have another question. When I use my SMILES data training, should I load your trained model and then train? Does generating the model also generate new molecules...