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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What do results like these imply in a LSTM classification problem?

I am training a LSTM network to learn from multiple time series, and the output from the network should be binary (or equivalently a probability score between [0, 1]...
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Predicting time series

I have a very large dataset (about a year of driving) which contains the following features: datetime with 1 second resolution - speed of car - GPS coordinates of the car in each time-step - average ...
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Degree of freedom for NLP DL models

How degree of freedom can be estimated for NLP use cases where put is high dimensional vector (let us say word2vec used and dim size is 500) say and RNN or 1D CNN is used for modeling?
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RNN for continuous, real-time learning without pre-training

I am learning ML and I'm trying to solve this problem Create a rock paper scissors game where the AI is able to beat the player more than 50% of the time. My initial intuition was to use an RNN with ...
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What counts as a token for bpemb's encode_ids_with_eos()

I have probelms understanding bpemb's encode_ids_with_eos() or similar. When I run the following code i get none-word like segmentations (rather syllalbus based or ...
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Can MLP model sequential data?

When modeling sequential data, RNNs are introduced as an improvement of MLP as they can model the time dependency between the inputs. It is said that feeding the last N data points in the sequence to ...
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pytorchs LSTMs use of 'bias' and 'weight' strings

Hi I am new to RNN and have come across this the following implementation of Pytorchs LSTM, but I cant understand how (or why) the 'bias' and ...
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How to use multiple parallel inputs for time series forecasting -- Pytorch

I'm currently working with the ECG recordings of several patients. I have the amplitude of the ECG for around 48 patients over the span of one hour, and I want to be able to forecast their future ECG ...
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Surrogate model for [parameter vector] to [time series]

Say I have a model $M$ that takes in a parameter vector $\beta$, and produces a (numerical) time series. This could be a complicated model (e.g. a bespoke enzyme reaction model), or something simple ...
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Contextual Embeddings LSTM DOUBT

I have a simple doubt. When we use Word2Vec, Its obviously a non contextual embedding because every word has a same representation. When I pass it to my LSTM, We say the hidden states are the ...
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Validation loss stays bouncing while training loss converges immediately

I'm using bi-GRU to try solving a bi-classification problem. What I have observed is that no matter how much dropout(from 0 to 0.6) and layer-norm I added, the training process shows similar situation:...
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Building a machine learning model to predict variables measured after the end of the crop based on environmental data

I have dataset (20 samples) containing timeseries on temperature, humidity etc (a total of 6 variables). Each timeseries is 5 complete days, which is 24 * 5 = 120 values. So dataset has hourly values ...
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DRQN Cartpole v-1 with decreasing reward

I'm trying to use RNN instead of feed-forward NN for the Cartpole-v1 problem but I cannot figure out why the reward seems to be decreasing. I thought the problem might be due to the fact that the ...
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What is the procedure for data preprocessing for time-dependent LSTM classifier?

I attempt a beginner level LSTM classification task with a time-series numerical data, but my task is finding changes in features over time (in which those changes describe the outcome or the classes),...
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Difference Between Attention and Fully Connected Layers in Deep Learning

There have been several papers in the last few years on the so-called "Attention" mechanism in deep learning (e.g. 1 2). The concept seems to be that we want the neural network to focus on ...
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Multivariate timeseries classification for each group in a dataset

Let's say, I have the following dataset: ...
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How To Shuffle Long-Short-Term-Memory Or Gated-Recurrent-Unit Layer Cells Operation?

As you know these types of layers operate side-to-side, and although could have been implemented in Bidirectional layer to operate on both forward and backward directions. But is it possible to change ...
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Validation error approaches the same value for many hyperparameters

I am using kerastuner to explore the parameter space of my RNN. The validation MSE for each model seems to follow the same trend: completely level at ~0.5, a major drop around the third epoch, then ...
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Why do RNN text generation models treat word prediction as a classification task?

In many of the sources I have found regarding text generation with word-based RNN models (LSTM or GRU), the model is trained to perform a classification task across the vocabulary (such as with ...
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Temporal rows selection for Recurrent Neural Networks

I have a time serie $x_{1},...,x_{n}$ with a temporal step $\Delta = date(x_{i+1}) - date(x_{i}) = (i+1) - (i)= 1 \ day $. For each $i \in [\![ 1,n ]\!] $, I know that the value of $x_{i}$ depends ...
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RNN basic doubt

Suppose if I have 2 sentences: "My name is Alex" "Alex is my name" If I am using a RNN, After processing both the sentences, Will the final output vector be the same? Because RNN ...
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Machine Learning Model to Translate an Input Time Series to a Target Time Series?

I want to train a machine learning model to translate input time series signals into target (ground truth) time series signals. I have thousands of input-target training pairs similar to the ones ...
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Is this article applying a wrong validation concept?

About this article Short-Term Photovoltaic Power Forecasting Based on VMD and ISSA-GRU Am I right in concluding that their proposal violates the neural network basic validation concepts by proposing ...
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What is the right Pytorch RNN implementation?

I read about RNN in pytorch: RNN — PyTorch 1.12 documentation. According to the document the RNN run the following function: I looked on another RNN example (from pytorch tutorial): NLP FROM SCRATCH: ...
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Multilayer/deep recurrent layer

I might be missing something, but I'm completely unable to find any reference about this topic. In the literature, there are many references about RNN, GRU, LSTM, STAR and many other architecture that ...
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Why can't I reproduce my results in keras using random seed?

I was doing a task using RNN to predict a time series movement. I want to make my results reproducible. So I strictly followed this post: https://stackoverflow.com/questions/32419510/how-to-get-...
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Why is LSTM so much more popular than GRU?

I read some papers and most of them have only used LSTM. I am doing a project using neural networks to predict stock movement. However, according to a friend of mine who is in the quant trading ...
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How to include current and past feature for prediction in RNN

I have a timeseries (sales for e.g.) and a correlated feature that impacts the timeseries. I want to reshape data so that I can also use current value of correlated feature as an input. I illustrate ...
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How to organize data to use a recurrent neural network LSTM?

I am doing an internship in bailiff society. I have to create an IA model which can improve actions to perform, based on existing timeline of actions. I've already tryed some solutions (which did not ...
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How to deal with different amounts of data every day?

I am doing a time series prediction task. There are different amounts of news headlines every day, and the goal is a binary prediction task to predict next day's stock movement. The amount of ...
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Weather impact on plant growth

I have a data set that includes the following and am using it to learn more about data science. I have googled a bunch - but can't seem to find any examples on what I am trying to do. I am trying to ...
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LSTM - Why is a sliding window so important in this problem?

I've been having performance issues with my LSTM implementation. Whenever I use a sliding window, the performance seems to get better. Moreover the size of the window seems to have a large impact on ...
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Help with Time Series prediction

I'm a complete n00b to both this stackexchange and ML so please don't flame me too bad. I am trying to make a prediction from Time Series data. I have about 10 years worth of 1-minute resolution price ...
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RNN to model DNA sequencing classification

I have a DNA sequence dataset each mapped to a certain class. e,g TCAGCCGAGAGCTCATCGATCGTACGT 2 ATGCAGTGCATCGATCGATCGTAGAAC 3 Where the number after the sequence specifies the type of protein this ...
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Time series classification but with a sequence in output

I'm using Python and I have a training set of sequences of this shape: (None, 9, 25), where: 9 are rows representing years from 2012 to 2020 25 are features. So each of this 25 features has a value ...
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What is the purpose of Sequence Length parameter in RNN (specifically on PyTorch)?

I am trying to understand RNN. I got a good sense of how it works on theory. But then on PyTorch you have two extra dimensions to your input data: batch size (number of batches) and sequence length. ...
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RNN/LSTM timeseries, with fixed attributes per run

I have a multivariate time series of weather date: temperature, humidity and wind strength ($x_{c,t},y_{c,t},z_{c,t}$ respectively). I have this data for a dozen different cities ($c\in {c_1,c_2,...,...
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PyTorch: LSTM training loss not decreasing; starting at very high loss

I am training an LSTM to give counts of the number of items in buckets. There are 252 buckets. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning ...
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Clarification on "predict the next character given the previous 100 characters"

I am studying Justin Johnson's lecture on RNNs Lecture recording: https://www.youtube.com/watch?v=dUzLD91Sj-o&list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r&index=12&t=3177s One of the examples ...
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How to predict a mathematical progression with keras

I try the following model for a many-to-many recurrent network: ...
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Training data for anomaly detection using LSTM Autoencoder

I am building an time-series anomaly detection engine using LSTM autoencoder. I read this article where the author suggests to train the model on clean data only in response to a comment. However, in ...
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LSTM Autoencoders vs LSTM

I'm working on a time-series anomaly detection project. I have read that both LSTM Autoencoders and LSTM can do the job. Can someone please help me understand what are the advantages of each i.e. when ...
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How are session-parallel mini-batches used for training RNNs for session-based recommender tasks?

I am reading this paper on session-based recommenders with RNNs: https://arxiv.org/abs/1511.06939. During the training phase, the authors apply what they call "session-parallel mini-batches,"...
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Error while Pre-processing Audio Data using Librosa (audio analysis library in python) for DL model

I am beginner in Audio classification field in DL. I followed a YouTube Music Genre Classification Series, which is working fine and been very helpful but I have a problem/error in pre-processing part....
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Does N-gram language model for text generation are more efficient than Neural Network language models?

I recently build an language model with N-gram model for text generation and for change I started exploring Neural Network for text generation. One thing I observed that the previous model results ...
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1D Sequence Classification using Circular Dilated Convolutional Neural Networks

I am working on a multiclass classification task on long 1D sequences. The sequence length may vary between $512$ and $512 \cdot 60$ timesteps, a slice of $100$ timesteps might look like this: What ...
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What model would be best suitable for Multi-variate Binary Classification?

My main objective here is classification, either a vehicle or pedestrian The Dataset I have is as follows, this was a data I collected using Radar for a vehicle going away from a radar , its produced ...
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Keras: ambiguity regarding state maintenance in RNNs

The following is mentioned in the official keras RNN documentation (https://www.tensorflow.org/guide/keras/rnn). By "Normally", I assume they mean when stateful=False, which is also the ...
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How are the weights defined in a (linear-chain) Conditional Random Field?

Edit: i saw that i mixed up i (in the graph) and t (in the formula), in the following i equivalent to t I am trying to understand the theory behind linear chain Conditional Random Fields. I have now ...
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How to use teacher forcing in a LSTM

For my timeseries problem it seems obvious to use teacher forcing. For example in the case of predicting the new timestep in a real life scenario, I do have access to all the ground truths for all ...

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