How to Construct Deep Recurrent Neural Networks AUTHORS: R. PASCANU, C. GULCEHRE, K. CHO, Y. BENGIO PRESENTATION: HAROUN HABEEB PAPER: HTTPS://ARXIV.ORG/ABS/1312.6026
This presentation Motivation Formal RNN paradigm Deep RNN designs Experiments Note on training Takeaways
Motivation: Better RNNs? Depth makes feedforward neural networks more expressive What about RNNS? How do you make them deep? Does depth help?
π π’ = π β (π π’ , π π’β1 ) Conventional π π’ = π π π π’ RNNs Specifically: β π π’ , π π’β1 ; πΏ, π½ = π β πΏ π π π’β1 + π½ πΌ π π’ π π π π’ ; πΎ = π π (πΎ π π π’ ) π βͺ How general is this? βͺ How easy is it to represent an LSTM/GRU in this form? βͺ What about bias terms? βͺ How would you make an LSTM deep?
THE DEEPENING
π π’ = π π π π’ DT(S)-RNN π π’ = π β (π π π’ , π π’β1 , π π’ , π π’β1 ) Specifically: π§ π’ = π(πβ π’ ) β π’ = π π ( π π πβ1 β¦ π π π 1 π π β π’β1 + ππ¦ π’ π π 2 1 + ΰ΄₯ π π β π’β1 +ΰ΄₯ π π π¦ π’ )
π π’ = π π π π’ DOT(S)-RNN π π’ = π β (π π π’ , π π’β1 , π π’ , π π’β1 ) Specifically: π π π (β¦ π π π 1 π π β π’ ) π§ π’ = π 0 (π π 1 β π’ = π π ( π π πβ1 β¦ π π π 1 π π β π’β1 + ππ¦ π’ π π 2 1 + ΰ΄₯ π π β π’β1 +ΰ΄₯ π π π¦ π’ )
0 = π β 0 (π π’ , π π’β1 0 π π’ ) sRNN (π) = π (π) (π π’ πβ1 , π π’β1 π βπ βΆ π π’ ) β (π) π π’ = π π π π’ Specifically: (π) π π’ = π π π π π’ (0) = π 0 (0) π π π’ + π π π π’β1 β π’ π 0 0 π = π π πβ1 + π (π) π π π’ π π π’β1 βπ: π π’ π π π
Experiment 0: Parameter count Food for thought: Not clear which one has most number of parameters β sRNN or DOT(S)-RNN.
Experiment 1: Polyphonic Music Prediction Next Task: note(s) Sequence of musical notes Food for thought: Sure, depth helps, but * helps a lot more in this case. What about RNN* and other models with *?
Experiment 2: Language Modelling Next Task : Sequence of characters/words character/word (LM on PTB) Food for thought: Deepening LSTMs? Stack them or DOT(S) them?
Note on training βͺ Training RNNs can be hard because of vanishing/exploding gradients. βͺ Authors did a bunch of things: βͺ Clipped gradients, threshold = 1 βͺ Sparse weight matrices ( π 0 = 20 ) βͺ β max π,π π π,π = 1 Normalized weight matrices βͺ Add gaussian noise to gradients βͺ Used dropout, maxout, π π units
Takeaways βͺ Plain, shallow RNNs are not great. βͺ DOT-RNNs do well. Following should be deep networks βͺ π§ = π(β, π¦) βͺ β π’ = π π π¦ π’ , β π’β1 , π¦ π’ , β π’β1 - both π and π βͺ Training can be really hard. βͺ Thresholding gradients, Dropout, maxout units are helpful/needed βͺ LSTMs are good Questions?
Recommend
More recommend
Sambuz
Unleash a World of Digital PossibilitiesβBrowse, Share, and Explore Content Without Boundaries
