Questions tagged [rnn]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
4 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: ...
1
vote
1answer
31 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 ...
0
votes
0answers
11 views

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. ...
0
votes
0answers
8 views

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 ...
2
votes
0answers
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 ...
1
vote
0answers
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 ...
2
votes
0answers
24 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, ...
0
votes
0answers
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 ...
0
votes
0answers
8 views

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 ...
0
votes
2answers
76 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 ...
0
votes
0answers
20 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 ...
0
votes
1answer
12 views

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 ...
0
votes
0answers
51 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 ...
0
votes
0answers
9 views

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 ...
0
votes
0answers
16 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 ...
0
votes
1answer
18 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 ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
8 views

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 (...
0
votes
0answers
10 views

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 ...
1
vote
1answer
24 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 ...
0
votes
1answer
39 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 ...
0
votes
1answer
29 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 ...
0
votes
0answers
6 views

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 ...
0
votes
0answers
13 views

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. ...
0
votes
1answer
80 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 ...
0
votes
1answer
29 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 $n$ ...
0
votes
1answer
19 views

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 (...
0
votes
0answers
24 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 ...
0
votes
1answer
27 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: ...
1
vote
0answers
21 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 ...
0
votes
0answers
154 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: ...
1
vote
0answers
33 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 ...
1
vote
1answer
38 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 ...
1
vote
0answers
11 views

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:...
2
votes
0answers
29 views

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 ...
0
votes
0answers
23 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]]? ...
0
votes
0answers
13 views

Vector to Sequence RNN

Does any one have any links to any implementations of a vector to sequence model? Preferably not in the domain of image captioning.
0
votes
0answers
13 views

encoding 100,000s of sparse, binary features at each time step of RNN

I’m looking for some pointers on efficiency. I have potentially 100,000s of binary variables that i wish to encode in each time step of an RNN for binary classification of the entire sequence, but I ...
1
vote
0answers
24 views

Machine Learning for analyzing and generating sentences from given text inputs

I'm trying to create a program that will translate Sign Language to Text and apply NLP so that the text is understandable to human. I've used CNN for recognizing sign language but I don't know how to ...
0
votes
1answer
32 views

Why is my neural language model performing so poorly?

I am trying to create a word-level Haiku generator using an LSTM neural network. I am scraping haikus from Reddit's r/haiku, and wanted to start with a "simple" model: my training data is ...
1
vote
0answers
23 views

Why we shift target(output) by one offset in language modelling

I have been working in sequence prediction tasks (very similar to language modelling) where I want to predict the next token(s)/item(s) given past sequence of tokens. I have always taken an approach ...
1
vote
0answers
14 views

Applying GradCam to video classification models

In the original paper, it says that GradCam visualization can be applied to any convolution based model. The problem is stated for convolutions that process images. In my case, I am classifying videos ...
1
vote
1answer
23 views

In what way is recurrent neural network state "hidden"?

Recurrent neural networks have hidden state denoted $h$. Why is the state considered "hidden"? It's clear to me what purpose the state itself serves, but I can't figure out why it is "...
0
votes
1answer
25 views

Does the SimpleRNN in Keras have a hidden state, or does it just use the output value as the hidden state?

When using tf.keras.layers.SimpleRNN,does this SimpleRNN have a hidden state, or does it just use the output value as the hidden state. That is, does it follow the ...
0
votes
0answers
28 views

How to use gradient checkpointing on packed sequence RNN

I have a batch of sequences that have a variable length. To save computation I used pack_padded_sequence as following: ...
1
vote
1answer
13 views

How can I predict the last element of the fixed length=8 sequence after I get each element?

There are fixed length lists [X1, X2, X3, X4, X5, X6, X7, X8] like this. I have many lists like them from the past. In the future, I will get new element of current list on weekly bases. one new ...
0
votes
0answers
16 views

multivariate biLSTM text classification

I have made a model that takes vacancy data and classifies it with (bi)LSTM. The variables of the initial dataset are: positiontitle ...
0
votes
1answer
13 views

Normalization of possibly not fully representative data

I am trying to train a classification RNN model on a sequence of table medical data, but I stuck with the normalization problem. I realized that I cannot simply use MinMaxScaler, because of 3 problems:...
0
votes
0answers
17 views

Encoding entities with features of continuous values

Given a set of entities, I would like to predict the next in the sequence; for this purpose, I would like to use RNN. However, my first challenge is how to model the entities. A possible input ...
1
vote
0answers
35 views

One Year Ahead Forecasting with Unevenly Spaced Time Series

I have many products in my warehouses which can be "demanded" any day by my different clients. I want to forecast how many of each item will be demanded for the whole next year. Naturally, ...

1
2 3 4 5
14