Questions tagged [lstm]

LSTM stands for Long Short-Term Memory. When we use this term most of the time we refer to a recurrent neural network or a block (part) of a bigger network.

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Can I send images + boundingboxes(as features) to an LSTM? How?

I have previously trained a YOLO v4 object detection model and I am looking to leverage the results(Bboxes) of this model and create another model to recognize/classify accidents in CCTV footage/video(...
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Train LSTM model with similar cyclic sequence

I am using keras LSTM to predict a seq2seq of 2 variables. I have test results for 50 subjects with ±20 tests per subject. the data is a 2 variable sequence with shape (101,2). as you can see, the ...
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Self Attention vs LSTM with Attention for NMT

I am trying to compare the A: Transformer based architecture for Neural Machine Translation (NMT) from the Attention is All You Need paper with B: an architecture based on Bi-directional LSTM's in the ...
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RNN/LSTM architecture for mapping one input variable to three output variables per timestep

I am trying to make a regressor that maps an timeseries with one input variable per timestep to 3 output variables per timestep. I am doing this to be able to predict the three output-variables in a ...
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Meaning of "Dependencies in "Long-term Dependencies" in LSTM?

LSTM is used instead of simple RNNs because it tackles Long-term dependencies, and solve vanishing gradient problem. I am confused a bit what is meant by the term "dependencies"?
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How to shape data when using LSTM autoencoder

I am working with simple data that has multiple features and a time stamp column. I have 24 hours of data across 70 days. The total number of samples is 1680. When applying a LSTM autoencoder, how ...
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My LSTM is struck with local minima

My LSTM Accuracy is low and is the same even if I go for higher epochs. I tried varying the optimizer/changing the batch size, but it still remains the same. My data: sequence length is 300, so its ...
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ValueError: Error when checking input: expected time_distributed_6_input to have 5 dimensions, but got array with shape (32, 224, 224, 3) [closed]

I am trying to apply data augmentation to avoid overffiting in my CNN-LSTM image classification model. My training data has the shape: (1882, 1, 224, 224, 3) My ...
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Applying LSTM or Deep Neural Algorithm for Mobile sensor

I am doing a project on mobile sensor Data ,I haven't used neural networks before on this type of data The data is 20750 subsamples extracted from the 1945 collected samples provided in a single .csv ...
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How forecasting with LSTM model with Keras with event-based sample data? Python

I would like to do anomaly detection on a sensor, my data is recorded according to a change in value of +/- X%. From one value to another I can have a very short or very long time difference. The ...
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How do I create series of windows for loading into Keras ConvLSTM?

I am trying to replicate the result of Pham et al. (2020) using Keras. The model architecture is built and training converges on a small subset of data, but now I need to feed it the full dataset for ...
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Error with MSE in LSTM

I'm trying to fit an LSTM model on my dataset, using also a validation set. My datasets have the following shapes: ...
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How to deal with over confident model?

I have an LSTM model for action recognition. During inference, any random actions that are not labelled or the model has not learned at all are also predicted with very high confidence score. I ...
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CNN-LSTM for price estimation

I have some pictures that I would like to extract features from using ResNet, and then apply another model like LSTM with extracted features to estimate the price. I have made two folders, one ...
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How to decrease LSTM RMSE?

I am using an LSTM model to predict the next measurement of a sensor. The dataset looks as follows: There are approximately 13000 measurements. My code for the LSTM looks as follows: ...
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How does state information get transferred when predicting with LSTM

I have a typical mutivariate time series forecasting problem that I want to solve using an LSTM, with mutliple features in the input sequnce and one feature in the output sequence. If I train my LSTM ...
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Architecture for ConvLSTM

I have an input data with 2000 samples each having shape of (5, 3, 178, 178) where 5 is time dimension, 3 is a color channel, and the rest are x and y-axis. Now I want to use ConvLSTM layer to predict ...
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Is an output layer with 2 units and softmax better than one with 1 unit and sigmoid for binary classification using LSTM?

I am using an LSTM for binary classification and initially tried a model with 1 unit in the output(Dense) layer with sigmoid as the activation function. However, it didn't perform well and I saw a few ...
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LSTM - unable to get a 3D output

I have an array with shape (55834, 250, 30) and I'd like to get an output of the same shape from this LSTM model. ...
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Multioutput prediction using LSTM encoder decoder with Attention

(I am working on Jupter notebook with python version 3.6.12, running Tensorflow 2.4.0 version.) I have a dataset that consists of 5 input features and 3 output features (that requires to be predicted)....
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Q: Training a CNN-LSTM on video inputs

Hello everyone! I implemented the following model, for action classification from videos, where each frame is 224x224x3, a video consists of ...
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problem with using f1 score with a multi class and imbalanced dataset - (lstm , keras)

I'm trying to use f1 score because my dataset is imbalanced. I already tried this code but the problem is that val_f1_score is always equal to 1. I don't know if I did it correctly or not. my X_train ...
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LSTM layer (keras) is causing all layers after it to constantly predict the same thing no matter the input

I have a model for OCR, which after 2-3 epochs gives the same output. When I predicted the values and looked at the output for each layer I realized that all layers after the 1st layer in the LSTM ...
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How can I train a LSTM with different time series of same process?

I have multiple time series dataset of the same process (e.g: sensor collecting humidity in a manufacturing process which last 2 hours) and would like to train a LSTM model to make forecast based on ...
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How to compare different forecasting models over different time horizon?

Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons. The time series data is ...
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How to apply one-to-many LSTM using Keras?

I am finding it difficult to wrap my head around the one-to-many approach using Keras LSTM block. I have 7 input parameters, using which I need to predict a sequence of length 650. I referred to LSTM ...
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Question regarding multivariate LSTM model

I am currently working on a multivariate LSTM model to forecast stock prices and am getting confused about how this model works. For univariate, it is straight forward. I have a dataset with only one ...
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Composite Input into Seq2Seq LSTM Network

Given that we have a seq2seq problem, where the input sequence is indeed multiple inputs and not only one as in traditional seq2seq problems. For example, in language translation, we usually give ...
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How does data shuffling work when LSTM is involved?

TIL that when using the LSTM layer, the states are remembered throughout the same batch. When using stateful LSTM, they can be even remembered outside of the batch. The first realization gave me a ...
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Models for Long-Term Time-Series Forecasting and Pattern Recognition

I'm trying to find a solution for long-term electricity hourly prices forecasting. Explaining simply, I have some data from 2018 - 2021 containing Demand, Renewable Generation, Hydropower Generation, ...
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Making use of several time series in one LSTM model

I am working on a case where I want to do a multivariate and multi-step time series forecasting. I have hourly data that measures temperature at approximately 500 different devices. (the devices have ...
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Time Series Forecasting with LSTMs in keras - convergence problem

I am trying to forecast a time series with multivariate input and multi output (multi step forecast). Since some of my input features are known for future time steps, wheras others are not, naturally ...
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FFNN vs. RNN for Regressing Physical Sensor Timeseries Data

I'm trying to build a network to regress data from one sensor to another. The target sensor is a scalar time series and the feature sensor can be either a scalar or vector time series. Both timeseries ...
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How to improve my deep LSTM model for time series?

I want to train a deep model for my time series power consumption dataset. I have created a model consist of CNN, BILSTM, Encoder-Decoder, and dense layers. here is my model: ...
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Backward LSTM in Pytorch

I'm in the process of rebuilding a network using PyTorch. The Keras implementation uses a LSTM module with the parameter go_backwards=true: ...
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Improve accuracy on LSTM - Multiclass Classification problem

Problem Description I need to build a model which solves the following problem. I have a sequence (let's say size=n) of integers (arrivals) , which looks like this 0,0,1,5,2,...,4,8,6 , and I want to ...
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Please explain Transformer vs LSTM using a sequence prediction example

I don't understand the difference in mechanics of a transformer vs LSTM for a sequence prediction problem. Here is what I have gathered so far: LSTM: suppose we want to predict the remaining tokens ...
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Why does this LSTM example and the y shape output I did come out differently?

I did the same as the example, but the y_predicted.shape output is different. is this example https://www.youtube.com/watch?v=s3CnE2tqQdo&list=PL7fNr5pdVm7UGJHLK5q0tZ6NZJqxGAofU&index=21&...
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Why is sequence prediction always the objective in RNN and LSTM like algorithms

The title is pretty much my question. I haven't seen any literature yet that uses a different training objective. The goal is to find the hidden states eventually, then why is it that only 1 method is ...
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Is it always beneficial to use return_sequences=True for time series prediction with RNN?

I roughly understand what return_sequences=True does when being used for time series prediction with RNN (each RNN cell outputs its hidden state). Now my question ...
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Multi site/source and Multivariate time series data (with multi time step) input in LSTM for forecasting

I am trying to make a multisite multivariate LSTM forecasting model with Keras. I have a simple Multivariate data structure like 3 X variables and 1 target variable with time-step 10, so my input ...
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Why the LSTM on Keras does not work correctly when it is necessary to predict several steps forward

I used AirPassenger Dataset. And based on several previous values(for examples 20) I want to predict several(3 or 5) steps in future. Like X -> y [10,20,30,....200]->[210,220,230] [20,30,40,.......
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Grouped Time Series forecasting with scikit-hts

I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
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LSTM model, poor performance

I have been working on a project on the demand for a product. I am using data from 2016 to train the LSTM model. The architecture is as follows: ...
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1answer
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How to Inference With Keras Sequential Models (Text Classification)

I have the following LSTM model and I can't make inference with it: ...
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Conceptual question - is it correct to use categorical variables such as day, month, year as a fixed sequence input in LSTM?

I am working on a problem where I have to try to predict the dependent variable (continuous) every hour based on hourly temperature (the single continuous variable in predictor space), along with 4 ...
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LSTM or GRU for Time-series Multilabel classification

Univariate time series data with only one feature vector (e.g. 1x1300 as a time step), a superposition or sum of different signals, should be disaggregated or ...
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How to extract skills from job description using neural network

I am doing a project where I have to extract skills from Job Description. I have attempted by cleaning data(not removing stopwords), applying POS tag, labelling sentences as skill/not_skill, trained ...
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LSTM and CNN - feature engineering and order for time series classification

My questions are related to multivariate time series classification, hence it may differ from forecasting problems. I can have either variable (entire history of the series) or fixed time steps (...
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Improving the accuracy of a Bidirectional LSTM model?

I have a model that I have spent the past few days trying to train and gradually improve. The data is of sequential nature, and I am trying to use an LSTM to classify the data as one of the three ...

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