Post on 21-May-2020
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Long-Short Term Memory Network
Hien Van Nguyen
University of Houston
11/6/2017
Why recurrent networks?
• Sequential input, next state depends on previous state
• Generalize to input with variable length
• Consider smaller chunk fewer parameters in model
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What is sequence?
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Source: https://uvadlc.github.io/lectures/lecture8.pdf
One-hot vector
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Recurrent networks
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Unroll through time
Recurrent networks
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Unroll through time
Simple recurrent network
• Linear activation
• Gradient:
• 𝑇𝑇 is the number of timestepsconsidered
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Problem of Vanishing/Exploding Gradient
• Review of chain rule
• Apply chain rule:
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How change in V at step k will affect loss at step t
On the difficulty of training recurrent networks https://arxiv.org/pdf/1211.5063.pdf
Problem of Vanishing/Exploding Gradient
• Recall that:
• Using chain rule:
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Problem of Vanishing/Exploding Gradient
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Long-Short Term Recurrent Networks (LSTM)
• Idea: Don’t multiply Multiplication == Vanishing gradients
Instead of multiplying previous hidden state by a matrix to get new state
we add something to old hidden state and get new state (not called “hidden state” but “cell” in LSTM language, explained next)
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Long-Short Term Recurrent Networks (LSTM)
• Intuition:Not everything is useful to rememberNot every input is useful to takeNot necessary to output each instance
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Long-Short Term Recurrent Networks (LSTM)
• Comparison of vanilla RNN and LSTM
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Vanilla RNN
LSTM
Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Long-Short Term Recurrent Networks (LSTM)
• Comparison of vanilla RNN and LSTM
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Vanilla RNN
LSTM
LSTM-Step by Step
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LSTM-Step by Step
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LSTM-Step by Step
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LSTM-Step by Step
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LSTM-Step by Step
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Long-Short Term Recurrent Networks (LSTM)
• Comparison of vanilla RNN and LSTM
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Vanilla RNN
LSTM
LSTM-Gradient Flow
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Learning sequence representation:https://d-nb.info/1082034037/34
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf
LSTM-Gradient Flow
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Applications – Machine Translation
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Source: https://uvadlc.github.io/lectures/lecture8.pdf
Applications – Machine Translation
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Google Pixel Buds
Applications – Image Captioning
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Applications – Question Answering
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Applications – Visual Question Answering
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Source: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf
Applications – Visual Question Answering
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