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Questions tagged [lstm]

A Long Short Term Memory (LSTM) is a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.

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Using RNNs to segment long streams

I'm using an LSTM to segment a long streams of linear data into contiguous parts. My specific application is extracting key signature changes from a stream of notes. Specifically, I want to (for each ...
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Examples of “one to many” for RNN/LSTM

Are there any examples dealing with "one to many" kind of LSTM? Basically I am trying to build a model which takes an input vector $a$ and gives an output of $[b_1; b_2 ;b_3; b_4, \ldots; b_n]$ where ...
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Formating input for LSTM (using Keras) [on hold]

I am working on a problem that predicts temperature and velocity profile along the length of a chamber based on its input values. I want to build a model which works as follows: Feed to network: <...
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Simple Keras LSTM model does not converge [duplicate]

I try to predict time-series with simple Keras LSTM model: ...
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23 views

Feature engineering for sheet music

I have a large dataset of digitized music scores that I'd like to use as input to a network. Initially, I'm looking to train networks to identify key signatures, tempo, dynamics, etc. from the raw ...
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8 views

How is the model uncertainty calculated? [on hold]

I have an LSTM model and I want to compute the uncertainty of my model. What formulas for uncertainty can I use? If there is an implementation in python, that'd be great.
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Text generation: Word embedding (vector) prediction instead of softmax probabilities?

0 I'm looking into a project that generates text using LSTM. The common approach to this is having the network output a distribution which you use softmax on to get the most probable word. This ...
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Understanding epoch, batch size, accuracy ,performance gain in lstm forecasting model

I am new to machine learning and lstm. I am referring this link LSTM for multistep forecasting for Encoder-Decoder LSTM Model With Multivariate Input section. Here ...
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13 views

RNN to predict completion of fixed-length time series

I need advice to model a certain kind of time series prediction for which I didn't find any existing solution. I have a large set of independent time series of fixed length (let's say 100 steps). All ...
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19 views

Tree-Paths as sequence input into a neural network

I'm currently trying to understand this paper but I struggle with the input into the NN. What I don't understand is what the input vectors should look like for the network described in b) in the image ...
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21 views

LSTM - When to use sliding window in time series classification?

Say I have a tensor of data with shape (30, 16000, 38) - where each tuple element corresponds to ...
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3 views

when not using teacher forcing does backpropagation flow through input layers to output layers?

When training a Recurrent Neural Network that feeds outputs in as the next input(no teacher forcing) do gradients flow through input layers to output layers and also through the hidden states?
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Attention mechanism in LSTM model to predict next action

I have a dataset. Each data point of this set contains a variable length of sequences with 7 letters. For example, Data point 1 has a sequence (A, A, B, E, B, C, D, E, D.....). I used LSTM to predict ...
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21 views

Binary Classification of Numeric Sequences with Keras and LSTMs [duplicate]

I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network. Each training example/sequence has 10 ...
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LSTM Generates Duplicates

I'm using an LSTM to generate molecules using the Simplified Molecular Input Line Entry System (SMILES) to represent molecules. As an example, Aspirin is represented as O=C(C)Oc1ccccc1C(=O)O I have ...
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13 views

Representative Pattern Extraction from Time Series using LSTM

I am interested in extracting a representative pattern from time series having variable time periods. I have attached an image for reference. I would like to know if a LSTM would be the appropriate ...
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Why my LSTM model cannot predict waveform

Basically, I'm trying to use one waveform (above) to predict another waveform (). And Here are my model structure: ...
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2answers
78 views

How does an LSTM process sequences longer than its memory?

* Note: The premise of my question was incorrect in the first place. My question assumes that an LSTM maintains a separate set of weights for each time step in the memory it is given as a design ...
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Adding context in a seq2seq RNN model

The encoder of a seq2seq model is meant to generate a conditioning context for the decoder, as mentioned here A RNN layer (or stack thereof) acts as "encoder": it processes the input sequence and ...
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LSTM - Multiple Time Series, degrading accuracy

I'm trying to make a LSTM model for detecting failures on a physical system, by supplying 27 features of sensor data. I've inputted three disjunct timeseries, each beginning with "normal" operational ...
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25 views

How is Bidirectional-RNN different from vanilla RNN trained with both original & reverse copies of data?

I have several questions regarding Bi-RNN. The RNN here can be LSTM or GRU as well. (1) What is the input of Bi-RNN when making inference? For RNN, if I want to predict a $\hat{y}(t)$ for the target $...
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Product Price Prediction - using online scrapped data [closed]

AIM: To Predict Price of products based on data that I have taken from other online stores. e.g Predict price of Samsung Galaxy S10, data will be from multiple online stores. Problem: Which Machine ...
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Binary target prediction using LSTM with sparse events in time

I have a data of patients that have multiple events happening in there medical history, I'd like to predict a target of having a specific targeted-event in the next 30 days. The data is timestamped ...
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What might explain inferior performance in a LSTM featuring a convolution layer?

This is not a problem:solution scenario insofar that I am not attempting to find a way to improve the model, merely find a reason for its behaviour. Model using LSTM has accuracy of about 84-86% ...
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how to prepare text data for LSTM autoencoder

My main goal is to come up with some topics using LSTM autoencoder. I want to use 20 news_group data set. after reading lots of material and looking at some GitHub project, I am still not clear how ...
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Shapes of input and outputs for LSTM architecture?

I have a sequence data like X1, X2, X3, X4, X5 -> y1,y2,y3,y4,y5 X6,X7,X8 -> y6, y7, y8 Where Xi is m x n dimension matrix, n is the number of columns (...
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LSTM time series forecasting on sparse dataset

I am working on the LSTM time series forecasting of solar energy production. The available data is one year on a half hourly basis. More than 60% of the data values are zero as the PV stations cannot ...
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How to model a time series problem for RNNs?

I have projects as input data, each project has a weekly progress report (hours of work completed in this week). A project can have an arbitrary duration, but let's say it's usually around 100 weeks, ...
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1answer
127 views

Cannot understand LSTM inference

I seem to have stumbled on a hole in my understanding around LSTMs. In short, I cannot understand how even a simple one is actually fed samples, upon inference time/training time. Here are the details:...
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15 views

Small output range and delayed output? Predicting sine using LSTM

I have coded a very basic LSTM with forget gates (no libraries used). I'm trying to predict 0.5*sin(t + N) given 0.5*sin(t) as an exercise. I have tweaked the model, changing the output layer ...
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1answer
29 views

Attention for short sequence length. Is it reasonable?

Will the attention mechanism be useful for the short sequence length? Let's say your training corpus has each query of MAX length 10. and most queries are of word length 3-4 words. How reasonable is ...
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Do we get the best performance with “batch_size = 1”(especially for LSTM)?

In my experience, choosing batch_size = 1 gives the best result and choosing the batch_size = whole data number gives the worst. ...
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67 views

Parameters Grid Search for Keras LSTM on Time Series

How do you do grid search for Keras LSTM on time series? I have seen various possible solutions, some recommend to do it manually with for loops, some say to use scikit-learn GridSearchCV. Feedback ...
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LSTM state-of-art [closed]

Can you advice me a good paper which talks about the LSTM's state-of-art? I have already searched on google but I have not found anything interesting Thank you very much
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Is it possible to make LSTM model with 4dimension shape?

Hellow, wizards. I have time series data including sevaral days. I try to predict a grade of tomorrow, which is range from 0 to 100. And I assume that this grade depends on 3 time-series independent ...
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172 views

Number of Hidden Layer Nodes in Recurrent Neural Networks

There's already a decent discussion on how to select the right number of hidden layers and hidden nodes in a feed-forward neural network: How to choose the number of hidden layers and nodes in a ...
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Time-series classification of Kinect data using Keras

For my PhD project I recorded using Kinect and Myo 11 people performing Cardiopulmonary Resuscitation (CPR), repeatedly doing chest compressions to a manikin (one person per time). I collected in ...
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Time Series Forecasting RNN: Difference Between Masking & Excluding Rows

Suppose you have missing values in a time series E.g. : t1 x1 y1 t2 ? ? t3 x3 y3 t4 ? ? t5 x5 y5 You are trying to forecast this time series using a recurrent ...
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33 views

Bias vector regularization in LSTM layer

Are there any scientific papers or articles on use of bias vector regularization for training LSTM models ( I am using Keras: https://github.com/keras-team/keras/blob/master/keras/layers/recurrent.py#...
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How to create dataset for speech recognition using librosa [closed]

I loaded the audio using librosa and extracted mfcc feature of the audio. I now have array of shape (20,N). How do I feed this as input to LSTM to predict?
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1answer
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How to train a RNN language model?

I want to train a RNN-based language model from https://arxiv.org/pdf/1409.2329.pdf for next word prediction. How to split the sentences from the dataset into input and ground truth during the ...
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1answer
53 views

Keras LSTM Long Term Dependencies

I am familiar with the LSTM unit (memory cell, forget gate, output gate etc) however I am struggling to see how this links to the LSTM implementation in Keras. In Keras the input data structure for X ...
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76 views

What is the initial state of the tf.contrib.rnn.LSTMCell? [closed]

Does tf.contrib.rnn.LSTMCell assign itself an initial state of zeros or is it random for each batch or per complete run through (if I run the model twice will it have the same initial state both the ...
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135 views

Batch size stateful LSTM in Keras - how to evaluate with just one sample? [closed]

I'm using Keras to classify time series of 1000 timesteps containing one feature. In keras the input shape of an LSTM is defined as (batch_size, timesteps, features). So my input is f.e. (32,1000,1), ...
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26 views

Variation in LSTM Model based on seeds

I am working on a time series project. The results of LSTM model which i am using varies a lot with the variation in seeds. I am wondering how can i make that model stable. Currently to get the ...
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38 views

Totally confused about input_shape dimension of stateful LSTM Network with keras [closed]

let's say I've got 2500 time series of class A, and 2500 time series of class B, both 1000 time steps long. There is only one numerical feature per time series. I want to train a LSTM using Keras to ...
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27 views

Can't get a Keras model to overfit [duplicate]

(Full disclosure, this is a follow-up to this question, which wasn't completely answered on StackOverflow) The input dataset is a time series of some stock price movement, but it might as well be ...
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What is the recommended maximum number of time steps for RNN or LSTM?

More time steps incurs longer training time, which above certain limit becomes impractical. What is the recommended maximum limit of the number of time steps for RNN or LSTM? I'm using a powerful ...
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1answer
73 views

forget_bias interpretation in tensorflow

In Basic LSTM cell of tensorflow there is an argument named forget_bias. From the documentation of ...