Gated Recurrent Networks

Transcription

1 Gated Recurrent Networks Davide Bacciu Dipartimento di Informatica Università di Pisa Intelligent Systems for Pattern Recognition (ISPR)

2 Outline Recurrent Neural Networks Gradient Issues Dealing with Sequences in NN t = 0 t = 1 t = 2 t = N Variable size data describing sequentially dependent information c 3 c N Neural models need to capture dynamic context c t to perform predictions Recurrent Neural Network Fully adaptive (Elman, SRN, ) Randomized approaches (Reservoir Computing) Introduce (deep) gated recurrent networks

3 Outline Recurrent Neural Networks Gradient Issues Lecture Outline RNN Repetita Motivations Learning long-term dependencies is difficult Gradient issues Gated RNN Long-Short Term Memories (LSTM) Gated Recurrent Units (GRU) Advanced topics Understanding and exploiting memory encoding

4 Outline Recurrent Neural Networks (RNN) Gradient Issues Unfolding RNN (Forward Pass) By now you should be familiar with the concept of model unfolding/unrolling on the data Graphics colah.github.io model q 1 data x 0 x 1 x 2 x t memory encoding unfolding Map an arbitrary length sequence x 0.. x t to fixedlength encoding h t

5 Outline Recurrent Neural Networks (RNN) Gradient Issues Supervised Recurrent Tasks output hidden input element to element sequence to item item to sequence sequence to sequence Graphics karpathy.github.io

6 Outline Recurrent Neural Networks (RNN) Gradient Issues A Non-Gated RNN (a.k.a. Vanilla) y t = f(w out h t + b out ) h t 1 h t h t i g t i h t = tanh(g t ) W h i h t 1 x t i W in x t g t h t 1, x t = W h h t 1 + W in x t + b h

7 Outline Recurrent Neural Networks Gradient Issues Learning to Encode Input History Hidden state h t summarizes information on the history of the input signal up to time t

8 Outline Recurrent Neural Networks Gradient Issues Learning Long-Term Dependencies is Difficult When the time gap between the observation and the state grows there is little residual information of the input inside of the memory What is the cause? J. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen, TUM, 1991

9 Outline Recurrent Neural Networks Gradient Issues Exploding/Vanishing Gradient Short story: Gradients propagated over many stages tend to Vanish (often) No learning Explode (rarely) Instability and oscillations Weights are shared between time steps sum gradient contributions through time h 1 h t 2 h t 1 δl t δh t y t h t δl t δw δh t 2 δh t 1 δh t x 1 δh t 3 x t 2 δh t 2 x t 1 δh t 1 x t Bengio, Simard and Frasconi, Learning long-term dependencies with gradient descent is difficult. TNN, 1994

10 Outline Recurrent Neural Networks Gradient Issues A Closer Look at the Gradient δl t δw = t k=1 δl t δh t δh k δh t δh k δw This is a parameter matrix we have a Jacobian Inside here you have chain rule δh t δh k = δh t δh t 1 δh t 1 δh t 2 δh k+1 δh k δl t δw = t k=1 δl t δh t t 1 δh l+1 l=k δh l δh k δw The gradient is a recursive product of hidden activation gradients (Jacobian)

11 Outline Recurrent Neural Networks Gradient Issues Bounding the Gradient (I) Given h l = tanh(w h h l 1 + W in x l ) then δh l+1 δh l = D l+1 W l T where the activation Jacobian is D l+1 = diag(1 tanh 2 W h h l + W in x l+1 ) δl t δh t = δl t δh k t 1 δhl+1 l=k δh l = δl t 1 t δh k l=k D l+1 W l T We are interested in the gradient magnitude δl t δh t

12 Outline Recurrent Neural Networks Gradient Issues Bounding the Gradient (II) δl t = δl t 1 t δh t δh k l=k D l+1 W l T δl t δh k t 1 l=k D l+1 W l T = δl t δh k t 1 l=k σ D l+1 σ W l T Bounded by the spectral radius σ Can shrink to zero or increase exponentially depending on the spectral properties σ < 1 vanishish σ > 1 exploding

13 Outline Recurrent Neural Networks Gradient Issues Gradient Clipping for Exploding Gradients Take g = δl t If g δw > θ 0 then g = θ 0 g Rescaling does not work for gradient vanish as total gradient is a sum of time dependent gradients (preserving relative contribution from each time δl t makes it exponentially decay) = t δw k=1 g δl t δh t δh t δh k

14 Gated Networks Long-Short Term Memory Gated Recurrent Units Tackling Gradient Issues Solution seems to be having the Jacobian with σ = 1 (activation function?) Linear dominated by the eigenvalues of W h to the power of t Linear with weight 1 (state identity) h t = h t 1 + c(x t ) Has the desired spectral properties but does not work in practice as it quickly saturates memory (e.g. with replicated/non-useful inputs and states)

15 Gated Networks Long-Short Term Memory Gated Recurrent Units Gating Units Mixture of experts the origin of gating + output Local Expert 1 Local Expert 2 Local Expert K Softmax Gating Network input x Jacobs et al (1991), Adaptive Mixtures of Local Experts,

16 Gated Networks Long-Short Term Memory Gated Recurrent Units Long-Short Term Memory (LSTM) Cell h t 1 h t Lets start from the vanilla RNN unit g t + x t S. Hochreiter, J. Schmidhuber, Long short-term memory". Neural Computation, Neural Comp. 1997

17 Gated Networks Long-Short Term Memory Gated Recurrent Units LSTM Design Step 1 h t 1 h t Introduce a linear/identity memory c t c t c t g t Combines past internal state c t 1 with current input x t x t

18 Gated Networks Long-Short Term Memory Gated Recurrent Units LSTM Design Step 2 (Gates) h t 1 c t 1 h t + c t Input gate Controls how inputs contribute to the internal state g t + I t (x t, h t 1 ) + Logistic sigmoid x t

19 Gated Networks Long-Short Term Memory Gated Recurrent Units LSTM Design Step 2 (Gates) h t 1 c t 1 h t c t + Forget gate Controls how past internal state c t 1 contributes to c t F t (x t, h t 1) + g t + + Logistic sigmoid x t

20 Gated Networks Long-Short Term Memory Gated Recurrent Units LSTM Design Step 2 (Gates) h t 1 c t 1 + h t c t + Output gate Controls what part of the internal state is propagated out of the cell + g t + O t (x t, h t 1 ) + Logistic sigmoid x t

21 Gated Networks Long-Short Term Memory Gated Recurrent Units LSTM in Equations 1) Compute activation of input and forget gates I t = σ(w Ih h t 1 + W Iin x t + b I ) F t = σ(w Fh h t 1 + W Fin x t + b F ) 2) Compute input potential and internal state g t = tanh(w h h t 1 + W in x t + b h ) c t = F t c t 1 + I t g t 3) Compute output gate and output state O t = σ(w Oh h t 1 + W Oin x t + b O ) h t = O t tanh(c t ) element-wise multiplication

22 Gated Networks Long-Short Term Memory Gated Recurrent Units Deep LSTM 1 h t 1 3 h t 1 1 h t h t 3 x t LSTM CELL LSTM CELL LSTM CELL 2 h t 1 h t 2 y t LSTM layers extract information at increasing levels of abstraction (enlarging context)

23 Gated Networks Long-Short Term Memory Gated Recurrent Units Training LSTM Original LSTM training algorithm was a mixture of RTRL and BPTT BPTT on internal state gradient RTRL-like truncation on other recurrent connections No exact gradient calculation! All current LSTM implementation use full BPTT training Introduced by Graves and Schmidhuber in 2005 Typically use Adam or RMSProp optimizer

24 Gated Networks Long-Short Term Memory Gated Recurrent Units Regularizing LSTM - Dropout Randomly disconnect units from the network during training N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014

25 Gated Networks Long-Short Term Memory Gated Recurrent Units Regularizing LSTM - Dropout Randomly disconnect units from the network during training N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014

26 Gated Networks Long-Short Term Memory Gated Recurrent Units Regularizing LSTM - Dropout Randomly disconnect units from the network during training N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014

27 Gated Networks Long-Short Term Memory Gated Recurrent Units Regularizing LSTM - Dropout Randomly disconnect units from the network during training Regulated by unit dropping hyperparameter Prevents unit coadaptation Committee machine effect Need to adapt prediction phase Drop units for the whole sequence! N. Srivastava et al, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, JLMR 2014

28 Gated Networks Long-Short Term Memory Gated Recurrent Units Regularizing LSTM - Dropout Randomly disconnect units from the network during training x x You can also drop single connections (dropconnect) x x x Regulated by unit dropping hyperparameter Prevents unit coadaptation Committee machine effect Need to adapt prediction phase Drop units for the whole sequence!

29 Gated Networks Long-Short Term Memory Gated Recurrent Units Practicalities Minibatch and Truncated BP Minibatch (MB) Training Data Average gradient on full data Stochastic GD sequenceby-sequence MB1 MB2 MBn δl MB1 δw δl MB2 δw δl MBn δw Truncated gradient propagation h k 1 x h k h k+1 h 1 h T

30 Gated Networks Long-Short Term Memory Gated Recurrent Units Gated Recurrent Unit (GRU) Reset acts directly on output state (no internal state and no output gate) h t 1 + h t h t = (1 z t ) h t 1 + z t h t h t = tanh(w hh (r t h t 1 ) + W hin x t + b h ) 1 Reset and update gates when coupled act as input and forget gates + z t + r t h t z t = σ(w zh h t 1 + W zin x t + b z ) + r t = σ(w rh h t 1 + W rin x t + b r ) C. Kyunghyun et al, Learning Phrase Representations using RNN Encoder- Decoder for Statistical Machine Translation, EMNLP 2014 x t

31 Advanced Models & Software Conclusions Bidirectional LSTM Character Recognition Character distribution 1 output for each character plus no output symbol LSTM layers Preprocessed input Original input A. Graves, A novel connectionist system for unconstrained handwriting recognition, TPAMI 2009

32 Advanced Models & Software Conclusions Bidirectional LSTM Character Recognition Character distribution 1 output for each character plus no output symbol LSTM left-to-right LSTM right-to-left LSTM layers Preprocessed input Original input A. Graves, A novel connectionist system for unconstrained handwriting recognition, TPAMI 2009

33 Advanced Models & Software Conclusions Generative Use of LSTM/GRU e l l Teacher forcing at training time LSTM3 LSTM2 LSTM1 Refeeding output at prediction time H e l A. Graves, Generating Sequences With Recurrent Neural Networks, 2013 Bypass connections

34 Advanced Models & Software Conclusions Character Generation Fun Shakespeare PANDARUS: Alas, I think he shall be come approached and the day When little srain would be attain'd into being never fed, And who is but a chain and subjects of his death, I should not sleep. Second Senator: They are away this miseries, produced upon my soul, Breaking and strongly should be buried, when I perish The earth and thoughts of many states.

35 Advanced Models & Software Conclusions Character Generation Fun Linux Kernel Code /* * If this error is set, we will need anything right after that BSD. */ static void action_new_function(struct s_stat_info *wb) { unsigned long flags; int lel_idx_bit = e->edd, *sys & ~((unsigned long) *FIRST_COMPAT); buf[0] = 0xFFFFFFFF & (bit << 4); min(inc, slist->bytes); printk(kern_warning "Memory allocated %02x/%02x, " "original MLL instead\n"), min(min(multi_run - s->len, max) * num_data_in), frame_pos, sz + first_seg); div_u64_w(val, inb_p); spin_unlock(&disk->queue_lock); mutex_unlock(&s->sock->mutex); mutex_unlock(&func->mutex); return disassemble(info->pending_bh); }

36 Advanced Models & Software Conclusions Generate Sad Jokes A 3-LSTM layers neural network to generate English jokes character by character Why did the boy stop his homework? Because they re bunny boo! Q: Why did the death penis learn string? A: Because he wanted to have some roasts case! What do you get if you cross a famous California little boy with an elephant for players? Market holes.

37 Advanced Models & Software Conclusions Understanding Memory Representation At Layer-3 neuron show some form of context induced representation of subsequences (words)

38 Advanced Models & Software Conclusions Understanding Memory Representation Neurons in early recurrent layers tend to organize according to sequence suffix

39 Advanced Models & Software Conclusions Recursive Gated Networks Recursive LSTM cell for binary trees Unfolding on parse trees for sentiment analysis

40 Advanced Models & Software Conclusions Encoder-Decoder Architectures The sequence to sequence framework Deep LSTM model LSTM activations used as encoding of the full input sequence to seed output sentence generation I Sutskever et al, Sequence to Sequence Learning with Neural Networks, NIPS, 2014

41 Advanced Models & Software Conclusions More Differentiable Compositions CNN-LSTM Composition for image-to-sequence (NeuralTalk) A cat is sitting on a toilet seat A. Karpathy and L. Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Descriptions, CVPR A woman holding a teddy bear in front of a mirror

42 Advanced Models & Software Conclusions RNN A Modern View RNN are only for sequential/structured data? RNN as stateful systems

43 Advanced Models & Software Conclusions Software Standard LSTM and GRU are available in all deep learning frameworks (Python et al) as well as in Matlab s Neural Network Toolbox If you want to play with one-element ahead sequence generation try out char-rnn implementations (ORIGINAL) on_tutorial.html

44 Advanced Models & Software Conclusions Take Home Messages Learning long-term dependencies can be difficult due to gradient vanish/explosion Gated RNN solution Gates are neurons whose output is used to scale another neuron s output Use gates to determine what information can enter (or exit) the internal state Training gated RNN non always straightforward Deep RNN can be used in generative mode Can seed the network with neural embeddings Deep RNN as stateful and differentiable machines

45 Deep Autoencoder Advanced Models & Software Conclusions Next Lecture Next week will have two practical lectures on deep learning frameworks Antonio Carta Pytorch Francesco Crecchi Tensorflow Coming up next Advanced DL topics Attention models, Variational Deep Learning, Midterm 2 discussions Thursday 3 rd May h Room N