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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1answer
264 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
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1answer
290 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 ...
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1answer
789 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 ...
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2answers
61 views

Problem extracting features from convolutional layer where the dimensions are big for feature maps

I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use them to train an LSTM. The problem is: the ...
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0answers
14 views

LSTM - What kind of data should contain every dimension of input LSTM matrix, where does specific dimension points to?

I am a beginner and I have a hard time understanding inputs and outputs of LSTM. So from the begining, I am trying to create multivariate input&output LSTM for time series forecasting. Thankfully ...
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1answer
34 views

What type of network for a repeated experiment

I have a problem where I have 9 data points that are collected every minute for 40 minutes, and, by the 40th minute, the solution would be either end up being black or white. I would like to set up a ...
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1answer
42 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)?
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1answer
45 views

Is my intuition about RNN wrong?

Until today, my intuition about RNN (LSTM/GRU) was that this is some kind of NN that can remember previous inputs. Consider a task where you need to predict 0 if the previous input was 1. For example: ...
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0answers
17 views

Are there any resources about using RL with RNN to produce Open AI Five-type of AI?

I want to make a minimal working version of Open AI Five. It seems it uses PPO with LSTM, but I don't know how to implement the actual code, and couldn't find any online tutorials for it. Are there ...
2
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1answer
96 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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4answers
706 views

Use Machine/Deep Learning to Guess a String

I want to be able to input a block of text and then have it guess a string within a predefined range (i.e. a string that starts with three letters and ends with five numbers like "XXX12345", etc). ...
3
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1answer
66 views

When working with time-series data, is it wrong to use different time-steps for the features and target?

When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target? Or should I use the next-day price as a target, and, after training, predict 3 ...
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1answer
54 views

What is the time complexity for testing a stacked LSTM model?

In the data preparation phase, we have to divide the dataset into two parts: the training dataset and the test dataset. I have seen this post regarding the time complexity for training a model. ...
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1answer
59 views

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

RNN and LSTM models have many architectures that can be modified. We can also compose their input and output data. However, in the examples that I found on the web, the inputs and outputs of RNNs/...
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1answer
50 views

Is my dataset a time series dataset? and should I use an LSTM?

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature "conductivity value". The length of the dataset is around a 1000 rows. I need to find ...
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1answer
304 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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2answers
17k views

Can LSTM neural networks be sped up by a GPU?

I am training LSTM neural networks with Keras on a small mobile GPU. The speed on the GPU is slower than on the CPU. I found some articles that say that it is hard to train LSTMs (and, in general, ...
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1answer
19 views

Is a recurrent layer same as LSTM or single-layered LSTM?

In MLP, there are neurons that form a layer. Each hidden layer gives a vector of number that is the output of that layer. In CNN, there are kernels that form a convolutional layer. Each layer gives ...
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4answers
8k views

What exactly is a hidden state in an LSTM and RNN?

I'm working on a project, where we use an encoder-decoder architecture. We decided to use an LSTM for both the encoder and decoder due to its hidden states. In my specific case, the hidden state of ...
2
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0answers
28 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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1answer
2k views

What is the difference between LSTM and RNN?

What is the difference between LSTM and RNN? I know that RNN is a layer used in neural networks, but what exactly is an LSTM? Is it also a layer with the same characteristics?
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1answer
48 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 ...
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1answer
44 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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1answer
117 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 ...
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0answers
70 views

Which ML algorithm is the best for predict the next PRNG generated numbers?

I have a homework. The task is to decide, if the PRNG generated lottery is attackable/crackable or not. Details: Lottery: There is a lottery game where you have to choose 8 numbers between 1-20 for ...
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1answer
160 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 ...
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1answer
362 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. ...
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1answer
67 views

Why is there tanh(x)*sigmoid(x) in a LSTM cell?

CONTEXT I was wondering why there are sigmoid and tanh activation functions in an LSTM cell. My intuition was based on the flow of tanh(x)*sigmoid(x) and the ...
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1answer
122 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 ...
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4answers
87k 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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0answers
85 views

Why does the number of input tokens to an LSTM have an impact on the convergence of Integrated Gradients?

Background I am computing the attribution scores for a simple LSTM model using Integrated Gradients. This method defines the contribution of a feature to a model prediction by integrating over the ...
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1answer
72 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 ...
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1answer
102 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
216 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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1answer
172 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 ...
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3answers
2k 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 ...
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0answers
12 views

How sensitive are LSTM's to random zero values in its target feature when training?

I have worked with lstm's in the past, specifically for time series forecasting. However, the target feature in these time series were relatively "stable". With the loosely defined "...
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0answers
10 views

Categorical Location based Time Series data Prediction using LSTMs

I have some time series data with ActivityType as location shared below. Each CaseID has 6 unique values with different ...
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2answers
941 views

What evaluation metric are used for sequence-to-sequence prediction problems?

I am solving many sequence-to-sequence prediction problems using RNN/LSTM. What type of evaluation metrics can be used for sequence prediction problems? One metric is the mean squared error (MSE) ...
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0answers
18 views

How Long Can BPTT Truncated?

I wanted to ask what is, in general, the maximum value (the order of magnitude) of the number of time steps I can back-propagate in the past using TBTT (Truncated Backpropagation Through Time) in an ...
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0answers
29 views

Is this a good implementation of this LSTM architecture?

I had been looking at some OCR problems and came across this presentation. I implemented it. In the presentation, there is the LSTM-Stack (diagram and algorithm, slide 32): Here is a visualization of ...
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0answers
17 views

Boundedness of cell states in MC-LSTM (mass-conserving LSTM)

I'm currently reading Hoedt et al's paper on mass-conserving LSTM. In the corollary it is stated that "[T]he memory cells, $c_k^\tau$, are bounded by the sum of mass inputs $\sum_{t=1}^\tau x^t+...
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1answer
771 views

Is LSTM a subcategory of RNN?

Is the LSTM-Architecture a subcategory of RNNs? Or are they totally different? Literature doesn't seem to be unitary on this. This figure appears to explain the models to be alternatives, but I ...
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0answers
478 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%...
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0answers
32 views

Machine learning with raw data alone / or raw data with its statistics

My question is very general and it does not originate from a specific problem. Let's assume that, through experience, we have learned that some statistical property of a set of data is important in ...
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1answer
27 views

Do LSTM in tensorflow work sequentially or in parallel

I have a basic understanding how a cell and a layer of an LSTM works. However, I get confused by what "number of units" (as termed in tensorflow) exactly means. A unit is, as far as I ...
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1answer
30 views

What type of neural network do you need if you want to detect an action or dynamic pattern instead of a static pattern?

Let's say that you want to detect if a man is running, walking, or dancing instead of just detecting a man still. What type of neural networks will you use for this purpose?
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1answer
47 views

Reinforcing Learning when action has no effect on the environment

I am trying to get my head around a problem where the action by the agent can not change the environment. Without going into details, my problem is about error correction in an stochastic environment. ...
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1answer
439 views

Is this LSTM model underfitting?

I think this model is underfitting. Is this correct? ...

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