Firstly, the requirements cannot be installed through conda because they are too outdated.
Keras always returns "AttributeError: can't set attribute" when trying to load the Decoder. This is due to the chemvae/tgru_k2_gpu.py Layer.
self.units = .. should be changed to any other names because Keras/Tensorflow does not allow to set attributes from the super class.
The same goes for the variable self.recurrent_dropout
After fixing both, I still get an error Layer decoder_tgru expects 3 inputs, but it received 2 input tensors. Input received: [<tf.Tensor 'decoder_gru2_1/transpose_1:0' shape=(None, 120, 488) dtype=float32>, <tf.Tensor 'decoder_true_seq_input_2:0' shape=(None, 120, 35) dtype=float32>]
I still cannot figure out what is wrong with layer. Should I use former TF, Keras Versions to make it work?
Firstly, the requirements cannot be installed through conda because they are too outdated. Keras always returns "AttributeError: can't set attribute" when trying to load the Decoder. This is due to the chemvae/tgru_k2_gpu.py Layer. self.units = .. should be changed to any other names because Keras/Tensorflow does not allow to set attributes from the super class. The same goes for the variable self.recurrent_dropout
After fixing both, I still get an error
Layer decoder_tgru expects 3 inputs, but it received 2 input tensors. Input received: [<tf.Tensor 'decoder_gru2_1/transpose_1:0' shape=(None, 120, 488) dtype=float32>, <tf.Tensor 'decoder_true_seq_input_2:0' shape=(None, 120, 35) dtype=float32>]I still cannot figure out what is wrong with layer. Should I use former TF, Keras Versions to make it work?