Questions tagged [long-short-term-memory]

For questions related to the long-short term memory (LSTM), which refers to a recurrent neural network architecture that uses LSTM units. The first LSTM unit was proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber in the paper "Long-Short Term Memory".

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25 views

When past states contain useful information, does A3C perform better than TD3, given that TD3 does not use an LSTM?

I am trying to build an AI that needs to have some information about the past states as well. Therefore, LSTMs are suitable for this. Now, I want to know that for a problem/game like Breakout, where ...
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23 views

Descent Training episodes for LSTM + TD3

I am building an AI with TD3 and lstm in both actor and critic. By LSTM size is 5,5 with 3 layers and hidden layers with 400 and 300 neurons respectively. I have states dimension of 5 with each value ...
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26 views

TD3+lstm predicting the same output for varying states

I have a model with TD3 + lstm in both actor and critic. I am trying to make it learn to predict some specific actions based on the environment conditions. However i see that the AI predicts very ...
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1answer
113 views

Number of LSTM layers needed to learn a certain number of sequences

Theoretically, number of units for a LSTM layer is the number of hidden states or the max length of sequences as per my practice. For example, in Keras: ...
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58 views
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30 views

Generating artificial data by means of LSTM

I got two classes namely positive and negative with 1500 samples on each a total of 3k. A sample sequence is like: ...
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34 views

Network design to learn multiple sequences of multiple categories

For learning a single sequence, LSTM only should suffice. However, my situation is different here. I have a list of sequences to learn: The sale volumes of 12 months, these are the sequences And ...
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1answer
31 views

How to exclude sections of bad data from time-series data before training an LSTM network

I am using LSTM network for predicting IOT time-series data receiving from un-reliable devices and networks. This results in several multiple sections [continuous streak of bad data for several days ...
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1answer
39 views

Text classification of non-equal length texts, should I pad left or right?

Text classification of equal length texts works without padding, but in reality, practically, texts never have the same length. For example, spam filtering on blog article: ...
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31 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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1answer
46 views

How to make a LSTM network to predict sequence only after input sequence is finished?

I am learning to use a LSTM model to predict time series data. Specifically, I hope the network should output a sequence (with multiple time steps) only after the input sequence has finished feeding ...
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36 views

Why doesn't my double deep Q network trained with the same training set give consistent performance?

I've written a Double DQN which can do either 1-step or multi-step learning. It also has a prioritised reply buffer. The internal network is an LSTM. My input data is a series of time series data and ...
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1answer
69 views

How do GPUs faciliate the training of a Deep Learning Architecture?

I would love to know in detail, how exactly GPUs help, in technical terms, in training the deep learning models. To my understanding, GPUs help in performing independent tasks simultaneously to ...
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1answer
398 views

What is the difference between LSTM and fully connected LSTM?

I'm currently trying to understand the difference between a vanilla LSTM and a fully connected LSTM. In a paper I'm reading, the FC-LSTM gets introduced as FC-LSTM may be seen as a multivariate ...
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17 views

Separated LSTMs or a global one for cluster of related features

I have an $n$-dimensional time-series to apply LSTM to, $n$ is the number of features for each time point. These features can be clustered according to their concept, for example $n_1, ..., n_4$ are ...
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2answers
140 views

Why are GRU and LSTM better than standard RNNs?

It seems that older RNNs have a limitation for their use cases and have been outperformed by other recurrent architectures, such as the LSTM and GRU.
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42 views

How to train an Encoder-Decoder LSTM for sequence to sequence prediction?

I have a dataset where for each country there is a name (string) and a multivariate time series (all integers). I am trying to use an Encoder-Decoder LSTM to forecast the next time steps in the time ...
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247 views

Do you have to add a dense layer onto the final layer of an LSTM?

If my understanding of an LSTM is correct then the output from each LSTM unit is the hidden state from that layer. For the final layer if I wanted to predict e.g. a scalar real number, would I want to ...
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1answer
47 views

How do LSTM or GRU gates learn to specialize in their desired tasks?

While I was studying the equations for the computation inside GRU and LSTM units, I realized that although the different gates have different Weight matrices, their overall structure is the same. They ...
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45 views

How to understand the matrices used in the Attention layer?

Attention-scoring mechanism seems to be a commonly-used component in various seq2seq models, and I was reading about the original "Location-based Attention" in Bahadanau well-known paper at https://...
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64 views

LSTM for imbalanced panel data

The available tutorials are most focused on time series prediction. I am wondering how shall we prepare the input data when it is an imbalanced data? Here is how data looks like. ...
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27 views

Did people analyze dynamics of very simple LSTMs?

I wonder if researchers tried to understand how LSTMs work by analyzing the dynamics of simple LSTM (e.g. with 2 units)? For example how the hidden state evolves depending on the properties of weight ...
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22 views

Visualisation for Features to Predict Timeseries Data

I have a course assignment to use an LSTM to predict the movement directions of stock prices. One of the things I am asked to do is provide a visualization to compare the predictive powers of a set of ...
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45 views

How does backpropagation work in LSTMs?

After reading a lot of articles (for instance, this one Understanding LSTM Networks), I know that the long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in ...
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23 views

Is the number of bidirectional LSTMs in seq2seq model equal to the maximum length of input text/characters?

I'm confused about this aspect of RNNs while trying to learn how seq2seq encoder-decoder works at https://machinelearningmastery.com/configure-encoder-decoder-model-neural-machine-translation/. It ...
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31 views

My LSTM text classification model seems not learn anything in early epochs

I am trying to use LSTM to do text classification and monitor the training process with tensorboard. But it seems that this model doesn't learn anything in early epochs. Is it normal for LSTM networks?...
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31 views

Which NLP model to use to handle long context?

I'm trying to process product data for an e-commerce platform. The goal is to understand products' size. Just to show you some examples on how messy product dimension description is: ...
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3answers
18k views

Why does the transformer do better than RNN and LSTM in long-range context dependencies?

I am reading the article How Transformers Work where the author writes Another problem with RNNs, and LSTMs, is that it’s hard to parallelize the work for processing sentences, since you have to ...
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1answer
54 views

Is there an LSTM-based unsupervised learning algorithm to label a dataset of curves?

I have a big amount of light curves (image below). I am trying to label the points as signal or background (the signal appears usually periodically, several times, for a given light curve). More ...
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179 views

Training RNN's on text: Can you use an ASCII encoding just as well as a one-hot character encoding?

I've mostly seen (e.g. in The Unreasonable Effectiveness of Recurrent Neural Networks) that when training RNN on text for something like language modeling, the text is usually featurized character-by-...
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37 views

Could zero-padding affect learning in a negative way?

I implemented an LSTM with Keras to perform word ordering task (given a syntactically unordered sentence, the goal is to label ...
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1answer
2k views

How to train a LSTM with multidimensional data

I am trying to train a LSTM, but I have some problems regarding the data representation and feeding it into the model. My data is a numpy array of three dimensions: One sample consist of a 2D matrix ...
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0answers
79 views

How to implement a LSTM for multilabel classification problem?

I would like to develop an LSTM because I have a variable input matrix. I am zero-padding to a specific length of 800. However, I am not sure of how to classify a certain situation when each input ...
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1answer
26 views

Inner working of Bidirectional RNNs

I'm trying to understand how Bidirectional RNNs work. Specifically, I want to know whether a single cell is used with different states, or two different cells are used, each having independent ...
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36 views

How do LSTM and GRU avoid to overcome the vanishing gradient problem?

I'm watching the video Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka where the author says that the LSTM and GRU architecture help to reduce the ...
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1answer
99 views

Do I need LSTM units everywhere in the network?

I have recently begun researching LSTM networks, as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and ...
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88 views

Do RNN solves the need for LSTM and/or multiple states in Deep Q-Learning?

Introduction I am trying to setup a Deep Q-Learning agent. I have looked that the papers Playing Atari with Deep Reinforcement Learning as well as Deep Recurrent Q-Learning for Partially Observable ...
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26 views

length independent sequence classification methods

I am looking to do sequence classification using deep learning. The length of my sequences can vary from a few hundred to several tens of thousands of characters. I was wondering what is a good ...
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1answer
196 views

What are pros and cons of Bi-LSTM as compared to LSTM?

What are the pros and cons of LSTM vs Bi-LSTM in language modelling? What was the need to introduce Bi-LSTM?
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31 views

Is there any way of generating fixed-length sequences with RNNs?

Is there any way of generating fixed-length sequences with RNNs? I want to tell my character level RNN to generate a name of length 3, 4, 5 and so on. I haven't found anything online like this, but my ...
3
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0answers
37 views

How to predict an event (or action) based on a window of time-series measurements?

I have an input vector $X$, which contains a series of measurements within a period, e.g. 100 measurements in 1 sec. The goal is to predict an event, let's say, moving forward, backward or static. I ...
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3answers
160 views

Using sigmoid in LSTM network for multi-step forecasting

I'm trying to develop a multistep forecasting model using LSTM Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using ...
2
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1answer
665 views

How to change the backward pass for an LSTM layer that outputs to another LSTM layer?

I am currently trying to understand the mathematics in Ger's paper Long Short-Term Memory in Recurrent Neural Networks. I have found the document clear and readable so far. On pg. 21 of the pdf (pg. ...
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46 views

Model for supervised sequence classification task

The Problem I am currently working on a sequence classification problem I try to solve with machine learning. The target variable is the current state of a system. This target variable is following a ...
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0answers
51 views

Can the cross-entropy loss be used for a NLP task with LSTM?

I am trying to build an LSTM model to generate Shakspeare-like poems. I have training set $\{s_1,s_2, \dots,s_m\}$, which are sentences of Shakespeare poems, and each sentence contains words $\{w_1,...
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1answer
58 views

Are sentences from the same document independent and identically distributed?

I am trying to build an LSTM model to generate Shakspeare-like poems. I have data set $\{s_1, s_2, \dots, s_m \}$, which are sentences of Shakespeare poems, and each sentence contains words $\{w_1, ...
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4answers
75k views

How to select number of hidden layers and number of memory cells in an LSTM?

I am trying to find some existing research on how to select the number of hidden layers and the size of these of an LSTM-based RNN. Is there an article where this problem is being investigated, i.e., ...
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49 views

How should I go about selecting an optimal num_units within a LSTM cell for different sequence sizes

I am currently working on a stock market prediction model which incorporates sentiments along with historical price for next day price prediction. I wanted to test different window / sequence size e....