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".

Filter by
Sorted by
Tagged with
0
votes
0answers
19 views

How to Classify Game Stages Based on Bitrate Time Series Data Using RNN - LSTM

I need suggestions for my project and would be glad if you would give me a hand. I have a dataset of frames obtained from the old-school game DOOM. Each frame in the dataset has the following columns: ...
1
vote
1answer
22 views

What is the input to the left most LSTM cell c(t-1) and h(t-1)?

Given an LSTM model with 3 cells shown below, what would be the input to the left most cell c(t-1) and h(t-1)?
0
votes
1answer
36 views

Advantages of CNN vs. LSTM for sequence data like text or log-files

When do you tend to use CNN rather than LSTM (or the other way round) in classification or generation tasks of sequential data like text or log-data? What are the reasons for the decision and what ...
0
votes
0answers
17 views

Converting inputs as a batch for time series classification would increase accuracy?

I have sensor dataset. I have already classified these data with LSTMs.I have a dataframe with 2 features and a class column. Assume that I take every two rows(inputs) respectively and make the ...
7
votes
3answers
1k views

Can the decoder in a transformer model be parallelized like the encoder?

Can the decoder in a transformer model be parallelized like the encoder? As far as I understand, the encoder has all the tokens in the sequence to compute the self-attention scores. But for a decoder,...
1
vote
1answer
22 views

How to train an LSTM to classify based on rare historic event?

I want an LSTM to output one of two classes (Y, N), per frame, based on all the input so far. My original inputs are very long (~100000 samples long, far more than a standard LSTM training can handle ...
1
vote
0answers
73 views

Building a template based NLG system to generate a report from data

I am a newbie to NLP and NLG. I am tasked to develop a system to generate a report based on a given data table. The structure of the report and the flow is predefined. I have researched on several ...
0
votes
0answers
12 views

PyTorch: LSTM error while trying to update the hidden state

I am trying to train an LSTM while keeping its hidden state (LSTM stateful) until the moment when I am going to start a new epoch(episode). But here it's come an interesting situation because I am ...
1
vote
1answer
31 views

How to define a “don't care” class in time series classification in Pytorch?

This is a theoretical question. Setup I have a time series classification task in which I should output a classification of 3 classes for every time stamp t. All ...
0
votes
1answer
21 views

My personal LSTM class in Keras is slow compared to the original one… Do you have some tricks? [closed]

I wrote a LSTM class with keras (see code below) but it is a little slower than the original one. It seems that it is slow to compile too... (it takes more time to load the model for training than ...
0
votes
1answer
41 views

Must all CNNs and RNNs not have a fully connected layer in order to be considered as such?

In the paper Wrist-worn blood pressure tracking in healthy free-living individuals using neural networks, the authors talk about a combination of feed-forward and recurrent layers, as if FC layers ...
0
votes
0answers
17 views

PyTorch: How to deal with hidden states of an LSTM?

I have a time series in which each date is correlated with the preview one, and base on that I am trying to predict action 1 and action 2. But the problem is that I am not sure how to deal with the <...
2
votes
1answer
61 views

How to represent integer values in sequence to sequence prediction task in encoder-decoder LSTM?

I have a large 2D grid having 30k rows and 35k columns, so a total of 30x35k grid cells. Each grid cell is represented by a unique integer number (identity of grid cell). I have several trajectories ...
1
vote
1answer
31 views

Do RNNs/LSTMs really need to be sequential?

There are many articles comparing RNNs/LSTMs and the Attention mechanism. One of the disadvantages of RNNs that is often mentioned is that while Attention can be computed in parallel, RNNs are highly ...
1
vote
1answer
40 views

Can RNNs get inputs and produce outputs similar to the inputs and outputs of FFNNs?

RNN and LSTM models have many interesting architectures that can be modified in various ways. We can also compose their input and output data in quite interesting ways. However, in the examples that I ...
0
votes
1answer
84 views

What is the big fuzz about SHA-RNN versus Transformers?

In his paper introducing SHA-RNN (https://arxiv.org/pdf/1911.11423.pdf) Stephen Merity states that neglecting one direction of research (in this case LSTMs) over another (transformers) merily because ...
3
votes
1answer
230 views

Why does the error of my LSTM not decrease after 10 epochs?

Despite the problem being very simple, I was wondering why an LSTM network was not able to converge to a decent solution. ...
0
votes
1answer
47 views

How can Image Caption work?

I have two models and a file contains captions for images. The output of model 1 is .pkl files contains the features of the images. Model 2 is the language model that will be trained with the captions....
0
votes
0answers
16 views

Multi dimensional LSTM modeling in KERAS

I have a database of time series signals with multiple features and Im trying to build a model to predict whether or not two samples are related to each other. For example : a database of 1000 sample ...
0
votes
0answers
8 views

Is it possible to get this loss with spikes, when training an LSTM with the cross-entropy on a multi-class classification problem of a time series?

The main question here will be "should I look for a bug?" My setup is a time series multiclass classification task, labeled per frame. I am using an LSTM, feeding inputs and using ...
2
votes
1answer
103 views

Price difference predictions curve almost vanished

With a team, we are studying how it is possible to predict the price movement with high-frequency. Instead of predicting the price directly, we have decided to try predicting price difference as well ...
0
votes
1answer
28 views

Convert LSTM univariate Autoencoder to multivariate Autoencoder

I have the following code snippet which takes in a single column of value i.e. 1 feature. How do I modify the LSTM model such that it accepts 3 features? ...
1
vote
1answer
52 views

time-series prediction : loss going down, then stagnates with very high variance

I am trying to design a model based on LSTM cells to do time-series prediction. The ouput value is an integer in [0,13]. I have noticed that one-hot encoding it and ...
2
votes
1answer
52 views

Is this LSTM layer learning anything?

I've trained a CNN-LSTM model but the results weren't satisfactory, so I took a look at my weight distributions and this is what I got: I don't understand. Is this layer learning anything? Or no? ...
2
votes
1answer
81 views

LSTM model on different time scales

I am a newbie to machine learning. I have an LSTM model that predicts the next output n+1 time 1, params 1, output 1 time 2, params 2, output 2 time 3, params 3, output 3 . . time n, params n, , ...
1
vote
0answers
20 views

Given the word embeddings, how do I create the sentence composed of the corresponding words?

I have done some reading. I want to implement an LSTM with pre-trained word embeddings (I also have plans to create my word embeddings, but let's cross that bridge when we come to it). In any given ...
4
votes
2answers
132 views

How should we regularize an LSTM model?

There are five parameters from an LSTM layer for regularization if I am correct. To deal with overfitting, I would start with reducing the layers reducing the hidden units Applying dropout or ...
2
votes
0answers
23 views

If I want to predict two unrelated values given the same sequence of data points, should I have a model with two outputs or two models?

I want to predict two separate y-values (not really logically connected) based on an input sequence of data (values x). Using LSTM cells. Should I train two models separately or should I just increase ...
1
vote
0answers
41 views

How to improve prediction performance of periodic data?

I have a 1 column dataset of $50 000$ points where 95% of the values equal $-50$. The data looks like the following: $$\begin{matrix} \text{time} & \text{value}\\ 1&-50 \\ 2&-50 \\ 3&-...
0
votes
0answers
23 views

How to make an LSTM ensemble model with different input shapes

This is what I got so far for making an lstm ensemble with one model input for each of the lstm models and for the ensemble model and it works perfectly. ...
0
votes
1answer
364 views

How to process data in a data stream for a LSTM

How can a data stream for a RNN (LSTM) be handled, when the stream contains data sets belonging to different prediction classes? Training phase: I have trained a LSTM to predict a class out of a ...
1
vote
0answers
142 views

What is the computational complexity in terms of Big-O notation of a Gated Recurrent Unit Neural network?

I have been digging up of articles across the internet in context of computational complexity of GRU. Interestingly, I came across this article, http://cse.iitkgp.ac.in/~psraja/FNNs%20,RNNs%20,LSTM%...
0
votes
1answer
37 views

Why won't my model train with CTC loss?

I am trying to train an LSTM using CTC loss, but the loss does not decrease when I train it. I have created a minimal example of my issue by creating training data where the network simply has to copy ...
0
votes
1answer
80 views

Is it possible to predict $x^2$, $\log(x)$, or variable function of $x$ using RNN?

There were some posts that using RNN can predict the next point of the sine wave function with data history. However, I wondered if it also works on all the functions of $x$, such as $x^2$, $x^3$, $\...
0
votes
0answers
34 views

Seq2Seq Modelling: when implementing some machine translation net, how are special tokens embedded?

When implementing any encoder-decoder network for machine translation, during training we provide the true output sentence to the decoder so that the context vector (from source language) may be ...
0
votes
0answers
72 views

How to make an ensemble model of two LSTM models with different window sizes i.e. different data shapes

Below is the Python code for making an ensemble model. All the inputs are the same for all three models. But what if the models have different input shapes due to different window size, such as LSTM ...
1
vote
1answer
74 views

How to understand marginal loglikelihood objective function as loss function (explanation of an article)?

I am reading article https://allenai.org/paper-appendix/emnlp2017-wt/ http://ai2-website.s3.amazonaws.com/publications/wikitables.pdf about training neural network and the loss function is mentioned ...
0
votes
0answers
99 views

How are temporal links made between following sequences in RNN?

Say I use an RNN, whatever is the cell's type, to perform time series classification. It can thus be seen as sequence classification. The time series is split into random, equal size, overlapping ...
0
votes
0answers
52 views

Why in RL function approximators with recurrent structures can learn planning?

In the paper An Investigation of Model-Free Planning, the authors use ConvLSTM to learn a planning function. In particular, for each input $x_t$ at time-step $t$, the function approximator is run for ...
12
votes
1answer
5k views

Which approaches could I use to create a simple chatbot using a neural network?

I wanted to start experimenting with neural networks, so I decided to make a chatbot (like Cleverbot, which is not that clever anyway) using them. I looked around for some documentation and I found ...
8
votes
3answers
1k views

Should I choose a model with the smallest loss or highest accuracy?

I have two Machine Learning models (I use LSTM) that have a different result on the validation set (~100 samples data): Model A: Accuracy: ~91%, Loss: ~0.01 Model B: Accuracy: ~83%, Loss: ~0.003 The ...
1
vote
0answers
23 views

Using LSTM model to train spatial inputs

I have an $x$-$y$ plane, inside that plane I have 9 paths $(p_1, p_2, \dots, p_3)$. Each path is classified into one of the three classes $(c_1, c_2, c_3)$. Each path has 100 coordinates points i.e $((...
0
votes
1answer
146 views

Feeding YOLOv4 image data into LSTM layer?

How would one extract the feature vector from a given input image using YOLOv4 and pass that data into an LSTM to generate captions for the image? I am trying to make an image captioning software in ...
2
votes
2answers
186 views

How to train a LSTM model with multi dimensional data

I am trying to train my model using LTSM layer in Keras (python). I have some problems regarding the data representation and feeding it into the model. My data is 184 XY coodinates encoded as a numpy ...
0
votes
1answer
45 views

Is the working of RNNs, LSTM and GRU sequential or parallel?

You take any blog or any example and all they tell you about is the given picture below. It has 4 different matrices and 3 of whose weights are shared. So, I'm wondering how is this achieved in ...
1
vote
1answer
628 views

How to use the LSTM layer in PPO architecture?

What is the best way of using the LSTM layer in PPO architecture? Should I use them in the first layer of both actor and critic, or use them just before the final layer of these networks? Should I ...
1
vote
1answer
232 views

Are 1D CNNs really the appropriate model for human activity recognition?

This article on human activity recognition states that 1D convolutional neural networks work the best on the classification of human activities using data from the accelerometer. But I think that ...
0
votes
0answers
13 views

Is it possible and if so does it make sense to have dense layers in between LSTM layers?

I am new to LSTMs and I was wondering if it is possible to have LSTM layer then dense then LSTM again and does it make sense?
13
votes
3answers
8k views

Where can I find the original paper that introduced RNNs?

I was able to find the original paper on LSTM, but I was not able to find the paper that introduced "vanilla" RNNs. Where can I find it?
3
votes
1answer
125 views

What's the difference between LSTM and GRU?

I have been reading about LSTMs and GRUs, which are recurrent neural networks (RNNs). The difference between the two is the number and specific type of gates that they have. The GRU has an update gate,...

1
2 3 4 5