DNNdumps

Some DNNs for getting a thesis.

Autoencoder

• AE/autoencoder.py

GAN

• GAN/gan.py

RNN

• RNN/LSTM_AE.py: sequence-to-sequence autoencoder
• RNN/Stacked_LSTM.py: with stacked RNN layers
• RNN/PriorLSTM.py: adding first timestep of the output sequence as input
• RNN/LSTM_UNIT.py: coupled sequence-to-sequence autoencoder
• RNN/OptionLSTM.py: explode output into n modalities, match with the best modality
• RNN/Option_Prior_LSTM.py: encode also the output to learn the modality distribution
• RNN/OptionLSTM_VAE.py: same as previous, but using VAE
• RNN/HierarchicalLSMT.py: encode and decode sequence of multiple lengths
• RNN/OptionalHierarchical_LSMT.py: combining Option with Hierarchy

Option LSTM

Hierachical LSTM

Points

• Training over all lengths increases the accuracy in finding the correct nearest neighbour using manhattan distance.
• Increasing the accuracy of the autoencoder increases is most crucial for getting good predictions.