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ECE 6504: Deep Learning for Perception Topics: LSTMs (intuition - PowerPoint PPT Presentation

Feb 27, 2023 •225 likes •410 views

ECE 6504: Deep Learning for Perception Topics: LSTMs (intuition and variants) [Abhishek:] Lua / Torch Tutorial Dhruv Batra Virginia Tech Administrativia HW3 Out today Due in 2 weeks Please please please please please

  1. ECE 6504: Deep Learning for Perception Topics: – LSTMs (intuition and variants) – [Abhishek:] Lua / Torch Tutorial Dhruv Batra Virginia Tech

  2. Administrativia • HW3 – Out today – Due in 2 weeks – Please please please please please start early – https://computing.ece.vt.edu/~f15ece6504/homework3/ (C) Dhruv Batra 2

  3. RNN • Basic block diagram (C) Dhruv Batra 3 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  4. Key Problem • Learning long-term dependencies is hard (C) Dhruv Batra 4 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  5. Meet LSTMs • How about we explicitly encode memory? (C) Dhruv Batra 5 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  6. LSTMs Intuition: Memory • Cell State / Memory (C) Dhruv Batra 6 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  7. LSTMs Intuition: Forget Gate • Should we continue to remember this “bit” of information or not? (C) Dhruv Batra 7 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  8. LSTMs Intuition: Input Gate • Should we update this “bit” of information or not? – If so, with what? (C) Dhruv Batra 8 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  9. LSTMs Intuition: Memory Update • Forget that + memorize this (C) Dhruv Batra 9 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  10. LSTMs Intuition: Output Gate • Should we output this “bit” of information to “deeper” layers? (C) Dhruv Batra 10 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  11. LSTMs Intuition: Output Gate • Should we output this “bit” of information to “deeper” layers? (C) Dhruv Batra 11 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  12. LSTMs • A pretty sophisticated cell (C) Dhruv Batra 12 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  13. LSTM Variants #1: Peephole Connections • Let gates see the cell state / memory (C) Dhruv Batra 13 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  14. LSTM Variants #2: Coupled Gates • Only memorize new if forgetting old (C) Dhruv Batra 14 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  15. LSTM Variants #3: Gated Recurrent Units • Changes: – No explicit memory; memory = hidden output – Z = memorize new and forget old (C) Dhruv Batra 15 Image Credit: Christopher Olah (http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

  16. RMSProp Intuition • Gradients ≠ Direction to Opt – Gradients point in the direction of steepest ascent locally – Not where we want to go long term • Mismatch gradient magnitudes – magnitude large = we should travel a small distance – magnitude small = we should travel a large distance (C) Dhruv Batra 16 Image Credit: Geoffrey Hinton

  17. RMSProp Intuition • Keep track of previous gradients to get an idea of magnitudes over batch • Divide by this accumulate (C) Dhruv Batra 17

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