An Introduction to Long Short-term Memory (LSTMs)
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The slides includes an introduction to Long Short-term Memory (LSTM ) >> A novel approach in dealing with vanishing gradients in deep neural networks. Made for students, and anyone out there who'd love to learn about recurrent artificial neural networks, specifically of the LSTMs architecture. Reference material has been attached to further your reading.
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![[1] Mastering Machine Learning with Python in Six Steps By Manohar
Swamynathan Bangalore
[2] Long Short-Term Memory By Sepp Hochreiter, Fakultät für Informatik,
Technische Universität München, 80290 München, Germany
[3] Long Short-Term Memory-Networks for Machine Reading, Jianpeng Cheng, Li
Dong and Mirella Lapata , School of Informatics, University of Edinburgh
[4] Long Short-Term Memory, M. Stanley Fujimoto, CS778–Winter2016,30 Jan
2016
References:](https://image.slidesharecdn.com/lstmpresent-230528054623-1521ad5f/85/An-Introduction-to-Long-Short-term-Memory-LSTMs-12-320.jpg)
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LSTM佳蓉 倪 Long short
term memory
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memory." Neural computation 9.8 (1997): 1735 1780.
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An Introduction to Long Short-term Memory (LSTMs)
- 1. LONG SHORT- TERM MEMORY Ssenjovu Emmanuel Joster Student-No: 2001200052 Bsc. Information Technology Faculty of Technoscience Dept. Computer Science & Electrical Engineering Muni University || P.O.Box 725, Arua(UG)
- 2. Artificial neurons are inspired by and modeled after the biological structure of the human brain ANNs are capable of learning to solve problems in a way our brains can do naturally. Artificial Neurons First things first... Connected neurons then form a network, hence the name neural network consisting of : input layer, hidden layer, and output layer
- 3. ● First step in a neural network. ● The network makes a prediction on what the output would be given an input. ● To propagate the input across the layers, we perform functions like that of below: a) Forward Propagation How ANNs work? A case of feedforward ANNs
- 4. ● Comes into play in training phase of a neural network. ● Involves adjusting the weights until network can produce desired outputs. ● We calculate error and its gradients with respect to each weight at each layer & subsequently adjust weights: b) Back Propagation How ANNs work?
- 5. ● Built upon ANNs like feedforward neural networks ● Have additional connections between layers making them eg feedback loop. ● ideal for sequential inputs eg text, music, speech, handwriting, change of price in stock markets. The previous step’s hidden layer and final outputs are fed back into the network and will be used as input to the next steps’ hidden layer, which means the network will remember the past and it will repeatedly predict what will happen next. Traditional RNN a) Forward Propagation Memory heavy, and hard to train for long-term temporal dependency
- 6. ● We calculate the error the weight matrices W generate, and then adjust their weights until the error cannot go any lower. ● To compute the gradient for the current , we 𝑊 need to perform the chain rule through a series of previous time steps. Because of this, we call the process back propagation through time (BPTT). If the sequences are quite long, the BPTT can take a long time; Traditional RNN ln practice many people truncate the backpropagation to a few steps instead of all the way to the beginning. a) Back Propagation Through Time(BPTT)
- 7. The Vanishing Gradient In multilayered ANNs eg RNN the vanishing gradient problem refers to the situation where the gradients(derivatives) of the loss function with respect to weights of early layers in a deep neural network become extremely too small to allow training for activities which require long- term dependency. For-example if we are predicting the next word in longer multi-sentence paragraph, it is less likely that that the model will be able to remember the first words in the beginning of the paragraph.
- 8. LONG SHORT-TERM MEMORY ● LSTM is an implementation of improved RNN architecture to address the issues of general RNN ● Enables long-range dependencies. ● Has better memory through linear memory cells surrounded by a set of gate units used to control the flow of information. ● It uses no activation function within its recurrent components, thus the gradient term does not vanish with back propagation.
- 9. The “Hidden Layer” Of an LSTM The hidden layer s made of a cell which s surrounded by gates that control the flow of information
- 10. 1.Forget Gate ● First step in which the sigmoid function outputs a value ranging from 0 to 1 to ● Determines how much information of the previous hidden state and current input it should retain. ● LSTM does not necessarily need to remember everything that has happened in the past. 2.Input Gate 3.Output Gate The gates of an LSTM The gates perform the following functions to control the flow of information Next step and involves two parts; ● First, the input gate determines what new information to store in the memory cell. ● Next, a tanh layer creates a vector of new candidate values to be added to the state. ● To determine what to output from the memory cell, we again apply the sigmoid function to the previous hidden state and current input, then multiply that with tanh applied to the new memory cell(this will make the values between -1 and 1)
- 11. Memory LSTM has an actual memory built into the architecture that lacks in RNN Vanshng/ Exploding gradients LSTMs can deal with these problems Accuracy More accurate predictions for larger sequential data Long-term dependency Able to capture complex patterns n huge datasets WHY LSTMs?
- 12. [1] Mastering Machine Learning with Python in Six Steps By Manohar Swamynathan Bangalore [2] Long Short-Term Memory By Sepp Hochreiter, Fakultät für Informatik, Technische Universität München, 80290 München, Germany [3] Long Short-Term Memory-Networks for Machine Reading, Jianpeng Cheng, Li Dong and Mirella Lapata , School of Informatics, University of Edinburgh [4] Long Short-Term Memory, M. Stanley Fujimoto, CS778–Winter2016,30 Jan 2016 References: