Questions tagged [rnn]

A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle.

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7 views

Training and Validation loss are same but not decreasing for LSTM model

I have a timeseries data and I am doing univariate forecasting using stacked LSTM without any activation function, Like following. ...
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Splitting sentiment analysis training data into x-train and y-train for a RNN?

Suppose I have a dataset of comments from users, around multiple websites, such that in each row, there are two comments, and one is considered more 'negative' and one more 'positive' indicated by the ...
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How to estimate time interval with external time-dependent regressors?

I'm on a team that is tackling a project similar to the following. Suppose you want to estimate the age of a plant using a small set of tabular data features. In addition to the plant data, you have ...
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7 views

Word-level text generation with word embeddings – outputting a word vector instead of a probability distribution

I am currently researching the topic of text generation for my university project. I decided (ofc) to go with a RNN getting a sequence of tokens as input with a target of predicting the next token ...
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21 views

How to predict data from sequence of sequences of variable size?

input data ...
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Dual Branch Recurrent Neural Network, what is the correct architecture and can I turn off one branch?

Let's suppose I want to predict the daily consumption of apples in the next 30 days of a person and I have, as input, the historical information about the past 60 days and the demographic information ...
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12 views

LSTM with variable time steps

I'm reading this post that describes how to train LSTMs with variable time step lengths. But does that have repercussions? Should I preprocess the time series in to varying permutations? e.g. Should ...
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10 views

Multivariant LSTM with labels per sequence

I'm very new to RNN and the ML space in general. Please excuse my lack of vocabulary and domain knowledge. I'm trying to classify requests as to whether if it's spam. The labels are per-user-based. ...
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15 views

One to many LSTM vs feed forward

Let us say I have three output variables I would like to predict $y_1, y_2, y_3$ where $y_1$ could have an high correlation with $y_2$ and $y_2$ with $y_3$. We have one input variable $x_1$. How does ...
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When does using RNNs make sense?

I have a very short multi-variate time series (10 to 20 time-steps) for which I want to perform a classification either at each sequence element (many-to-many) or at the end (many-to-one). Such that ...
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Should non-stationary time series be differenced when fit through neural networks?

I am fitting a recurrent neural network (RNN) on some non-stationary time series data. I know that, in the case of linear models, it is common practice to difference the series in order to make them ...
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Training neural network for regression with gaussian output layer

How does one train a neural network model that does regression over real values, using a gaussian output layer? ie estimating the mean and std parameters of the prediction. Since during training there ...
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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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141 views

How to predict a certain time span into the future with recurrent neural networks in Keras

I have the following code for time series predictions with RNNs and I would like to know whether for the testing I predict one day in advance: ...
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45 views

Transferring the hidden state of a RNN to another RNN

I am using Reinforcement Learning to teach an AI an Austrian Card Game with imperfect information called Schnapsen. For different states of the game, I have different neural networks (which use ...
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Does this make data leakage in time series? # need help for understanding time series data

Does this make data leakage in time series? I already read this, data leakage when scaling time series Data leakage is when information from outside the training dataset is used to create the model. ...
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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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19 views

How to model prior informaton in sequential models?

Are there any approaches to model prior information in sequential models? Such as in Sequence classification. For example, I have an input sequence [[Z, 0, 1], [Y, 1, 1]]. I need to classfy this into ...
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21 views

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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25 views

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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7 views

How to inference of time series with RNN(like LSTM, GRU etc)

Say I am doing a time series prediction which predict some value for next time step with past T inputs from historical inputs. Say I am using a RNN module like LSTM or GRU. In trainning/validation, I ...
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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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199 views

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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50 views

Is is possible to make a text generator with sklearn?

So recently I made a Tensorflow model using RNN (Recurrent neural networks) and I was wondering if it was possible with sklearn too, through the usage of SVMs or Naive bayes. I searched up many ...
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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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124 views

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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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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26 views

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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1answer
34 views

How does an RNN differ from a CBOW model

CBOW: We are trying to predict the next word based on the context (defined as a certain window of words around the target word) RNN can also be used for predicting the next word in a sequence, where ...
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Not using an input feature during a model evaluation process that was used in the training process

Assuming I have a dataset that consists of three different types of data column: x, y and z. x and y are data retrieved from sensors, where z is inputted manually. The goal of the deep learning model ...
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Backpropagation in RNN in discrete visible units

Refer to https://www.reddit.com/r/MachineLearning/comments/40ldq6/generative_adversarial_networks_for_text/ Goodfellow said that we still don't have a way to use GANs in NLP because of its discrete (...
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Non-constant/variable input data matrix length

Which neural network type could be used for input data matrix M (presented in the picture), where k dimension is constant, and n dimension is variable? A sequence of rows in n dimension does not have ...
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1answer
50 views

LSTM model with exogenous factors

I have the following 3 columns in my dataset: 1.month, 2.day_of_week, 3.quantity. I would like to predict the future values of quantity, having following variables as explanatory: One-hot encoding of ...
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1answer
76 views

Whats the minimum size sample required for a LSTM RNN model? [closed]

I have a data set of 100 rows x 100 to 300 columns. Will an LSTM RNN model work for my data or do I need more data? If my sample size is a problem are there other RNN architectures capable of modeling ...
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1answer
47 views

How to eliminate Non-Trainable params in Deep Learning [closed]

First of all, I would like to know what is the cause of Non-Trainable parameters? Secondly, how do you eliminate them? I used a combined CNN-RNN, it returned that 130 Non-Trainable parameters. Thank ...
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Multiple linked features in an LSTM RNN

I have a self-taught knowledge of machine learning and have been using Keras in Python 3.8. I am trying to create a LSTM RNN model for prediction of time-dependant sequential data. My data is of ...
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LSTM for classification where each sample is a time series of fixed length

I am trying to classify the Pavia University HSI data using LSTM. The dataset is an image of dimension (610,340,103). There are 610, rows, 340 columns and each pixel has 103 values. ...
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1answer
84 views

Very low error during training of a RNN for forecasting but high test error

I use a Recurrent Neural Network for time series forecasting of electrical load data from a cooling device based on past values of the load time series and temperature values. I first normalize the ...
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1answer
32 views

ML algorithm for high dimensional time series forecasting

I'm trying to make a forecasting model for goods prices in an economy (trying to forecast inflation). Dataset: has 300 goods prices % monthly variations for last 6 years. And also added $n$ ...
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1answer
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Can anyone interpret this Recurrent Network Encoder-Decoder question?

I'm trying to earn some extra credit, so the professor won't elaborate further on what's being asked in this question: The dataset that we're given is a line-by-line file of protein sequences (...
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26 views

Recurrent models for asynchronous / mixed frequency time series

What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.? So far I have found phased lstm from a similar ...
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1answer
37 views

Model layer getting random two input instead of 1 input

I am running the code mentioned at link of the code Here is the code: ...
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28 views

how to calculate parameters of an RNN using backpropagation

I'm trying to find out the two binary inputs are identical or not using RNN. my architecture is like this: I have the following functions: Where vT is the transpose of vector v and the activation ...
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367 views

ValueError: Layer model expects 2 input(s), but it received 3 input tensors using generator

I am trying to fit a model using generator function and I get the following error: ...
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41 views

Is it possible to combine cnn and rnn?

I would like to know if it is possible to combine rnn and cnn. I explain you : I have pictures of bikes, cars and moto and every pictures is linked to a text. For instance for a car I can have the ...
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1answer
141 views

MY lstm has a really low accuracy, is there anyway to improve it?

I am trying to make a model to classify whether these patients can be diagnosed with dementia by their 35 days of biometric data. A brief summary of a dataset is below. as an input X_train data, it ...
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In Keras, how to organize multiple input features using pre-trained embedding mapping?

Let's say the goal is to predict weather given multiple features (temp, humidity) in the past 3 days. weather (y) can be: Sunny, Cloudy, Rainy. Temp (X1) can be : Hot, Cool, Cold. Humidity (X2) can be:...
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Why do RNNs share weight?

If weights are not shared the number of parameters will be extremely large and difficult to compute which I understand. I don't understand the argument that varying length inputs are taken care of by ...
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31 views

How to walk forward an LSTM autoencoder by n timesteps?

I am able to fit this autoencoder to my sequence in order to reconstruct it. However, how would I be able to walk this forward 3 timesteps to get [[11.0], [12.0], [13.0]]? ...

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