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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LSTM state-of-art
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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8 views
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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7 views
Feeding Embedding Vectors With Time Series Data to LSTM [on hold]
I have daily sale data of some retail products on a three year span and I want to build a model that can predict future sales for all of these products. While looking for a way to forecast multiple ...
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13 views
Keras LSTM for timeseries forecasting help with error messages [on hold]
I am trying to understand how to use keras for supply chain forecasting and i keep getting errors that i can't find help for elsewhere. I've tried to do similar tutorials; sunspot forecasting ...
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23 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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20 views
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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12 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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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
42 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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1answer
21 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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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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23 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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23 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
26 views
forget_bias interpretation in tensorflow
In Basic LSTM cell of tensorflow there is an argument named forget_bias. From the documentation of ...
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1answer
19 views
image caption generator
I see two models of image caption generator online:
In the above model, the first LSTM cell of decoder takes the entire image as an input.
In the above model, all the LSTM cells of the decoder take ...
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38 views
LSTMs and Opening/Closing Brackets
I'm training a character-level LSTM to generate molecules using the SMILES system. Each molecule is represented as a string of characters, looking something like this:
Cn1c(Nc2c(Cl)ccc(CNC(=O)C(C)(C)...
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39 views
Which are the benefits of using ARIMA vs LSTM for time series forecasting? [duplicate]
I have already read this question: https://datascience.stackexchange.com/questions/12721/time-series-prediction-using-arima-vs-lstm
but I want to know in which circumstances is better ARIMA and in ...
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19 views
Way build a ML model with dynamic dataset have dependencies between feasuers- python or node
I need build a model has to predict for the director of a treatment institute
What is the recommended treatment for a new patient according to his Personal Information and Difficulty diagnosed:
the ...
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15 views
how the long term memory works in an LTSM network
I am trying to understand how lstm networks work in particular the long term memory aspect.
For example if we have a very long paragraph and at the start we have someone's name, Bob for example. Bob ...
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22 views
Backpropagation through LSTM and MLP
For didactic reason, I am currently implementing in numpy an LSTM network for classifications. I need to add on top of the LSTM another fully connected layer, because I don't want the output to have ...
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1answer
27 views
LSTM - random and always-different time between data measurements?
I am working with a time series problem where the time between two data measurements is random, and I am trying, without luck, to find an LSTM architecture that can handle this.
A very simplified ...
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1answer
41 views
Selecting number of time lags for input in LSTM networks?
I know from theory that LSTM are meant to selectively capture long and short term dependencies in a sequence. I'm trying to implement LSTMs for a time series task and I notice that a lot of tutorials ...
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2answers
41 views
Use LSTM to predict large number instead of poboabilities
I want to predict datasets like this
I'm using Keras' LSTM:
Not like a classification problem which returns probabilities, this model needs to output the exact value depending on input giving to it. ...
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25 views
How to encode text and categorical variables together?
I have two groups of texts that are very similar (e.g. reviews written on fridays and reviews written on mondays), and I want to build a LSTM that can classify them into positive and negative reviews. ...
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1answer
28 views
the appropriate batch size for ltsm
I am new to machine learning. I am looking at using time series data with a recurrent neural network in particular LSTM.
My question is to do with batch sizes. As I understand LSTM have long term ...
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1answer
27 views
About RNN with variable length output vectors
I have several thousands samples with equal number of features (5000, they are time dependent) and I would like to predict of vectors with variable length.
1) I'm beginner in RNN, and I'd like to ...
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25 views
How to get the same output on each training iteration of neural network?
I'm a neural network noob and am trying to use LSTM(Keras,Tensorflow backend) for predicting time series data. Every time I train the network, I get a very different set of output values even though ...
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2answers
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Is there any relation between number of hidden layers in a neural network and Performance?
I need to know if the number of hidden layers effect the performance and accuracy of a neural network, in other words does the increasing in the number of hidden layers of a neural network increase ...
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How to split data for stateless LSTM in Keras? [duplicate]
I have been thinking about the way of splitting the data into training, validation and test sets for the stateless LSTM. For me, the intuitive way is to arrange the original data into the 3D form (...
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1answer
25 views
How to diagnose loss curve not converging?
I am trying to predict remaining useful life (RUL) from temporal data with multilayered LSTM and obtaining the following curve:
Looks like after first several epochs performance stops to improve and ...
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1answer
22 views
How to get consistency in neural network and eliminate possibility of NaN values?
I'm using a neural network(Keras,LSTM) for time series regression. Whenever I run the network, I get different outputs for the prediction. This is presumably due to the randomised weight ...
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0answers
60 views
LSTM : multi-step multidimensional multivariate multi-site timeseries forecasting [closed]
I'm working on a project in which i'm trying to do a pollution forecasting.
I googled around and found that LSTM is a good candidate for this task, however, I'm still struggling at how to adapt it to ...
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1answer
55 views
Forecasting algorithms for incomplete time series data [duplicate]
I want to forecast the demand of each SKU in my warehouse every week from the history transaction that I have collected. The data contains brand, product type, SKU, quantity, date(per day), price. But ...
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1answer
24 views
How to paraphrase and augment training data for a question answering ML model?
I have only 50 question, answer pairs in my training data, where each question represent a unique intent. However, the training data is too small to build any meaningful ML model.
What are the ...
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1answer
77 views
LSTM output dimensionality
I am new to LSTMs. When reading the papers and websites about LSTM architecture, there is something I do not get.
As I understand it, a single LSTM layer can have multiple LSTM cells (just like a ...
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37 views
How to apply dropout in LSTMs?
Dropout in fully connected neural networks is simpl to visualize, by just 'dropping' connections between units with some probability set by hyperparamter p.
However, how dropout works in recurrent ...
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31 views
Reduce the effect of excessive zeros
I am working on an autoregression problem where I use sequential LSTM. My target is well defined, but I think I am facing a problem with the features. As the features were non-stationary, then I ...
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33 views
Getting over bid-ask bounce
High-frequency financial data is subject to bid-ask bounce.
Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in the form of ...
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19 views
Limiting regression accuracy
I am currently using a LSTM Network to solve a regressional problem. The goal is to predict the payload mass using various time series as input data. For our intents and purposes estimating the mass ...
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38 views
Zero inflated binomial for excessive zeros
"What are some tricks for dealing with a zero inflated response variable when tackling a machine learning regression problem?"
Answer :
"One of the easiest and most intuitive methods is to run a ...
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2answers
56 views
Deep learning methods with seasonal data
I have been building an LSTM model in Python which will predict the number of passengers arriving at a station in the next 15 mins.
My dataset is arrivals at the station every 15 mins across a 50 day ...
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1answer
479 views
Time steps in Keras LSTM
My understanding of time-series LSTM training is that the recurrent cell gets unrolled to a specified length (num_steps), and parameter updates are back-propagated ...
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3answers
138 views
Dealing with excessive number of zeros
ipdb> np.count_nonzero(test==0) / len(ytrue) * 100
76.44815766923736
...
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2answers
50 views
Why is LSTM so ineffective at this ridiculously simple sequence?
I am trying to predict the the sequence $(0.1, 0.05, …, 1.1)$ from the sequence $(0, 0.05, 0.1, …, 1)$. I thought this would be the only item in a toy dataset.
Now I implemented a long-short term ...
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92 views
Adding noise to time series data to increase training data
I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...
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61 views
What are paralell training and attention mechanism?
I read a quite interesting paper here:
http://hanj.cs.illinois.edu/pdf/kdd18_cyang.pdf
Accordingly, the basic idea is to combine clustering and churn prediction so that it can imply some insight from ...
