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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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Fuzzy Name Matching with Machine Learning. Input data encoding

I have a huge amount of data in my dataset: Last name, first name, date of birth of Indian residents and I need to match them for similarity. The matching is fuzzy, the data looks like this (names are ...
ккк ккк's user avatar
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What is the shape of the hidden/cell state of convLSTM2D?

I am new to convLSTM2D and I understand how it works, however, I am confused about the shape of the hidden states at different epochs ...
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recognition of names, surnames and patronymics

is there an example of neural networks on Github or Kaggle that perform the task of recognizing identical surnames, first names and patronymics? I'm just learning neural networks so it's interesting ...
nanana's user avatar
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Determining the threshold value for the neural network

I have a dataset with last name, first name, middle name of people participating in sporting events. I need to train a neural network that will match similar surnames, first names and patronymics. But ...
nanana's user avatar
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Deep neural network is plateauing on a regression task

I'm training a deep neural network on temporal graph data. Currently, I'm trying to get a feel for how large / complex of a model I should aim for, so I'm trying to overfit to my smallest dataset. ...
aadithyaa's user avatar
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Call volume prediction using LSTM and GRU

Machine Learning call volume prediction using LSTM and GRU I am trying to predict the number of incoming calls using LSTM and GRU I have done all the data preprocessing but upon training the model I ...
Kuda Kulrider's user avatar
1 vote
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Are formulas in the article incorrect?

I am learning about backpropagation in LSTM. I have been studying an article and watching two videos on the topic. The videos 1 and 2 repeat all the information from the article, but with additional ...
Тима 's user avatar
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Drum sound classification using RNN issues - help needed

I am new to the field of machine learning, even tho I have solid background in semi-related fields (am control system engineer by trade) and as a hobby project I wanted to work a bit with sound ...
APasagic's user avatar
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Adding sliding window dimension to data causes error: "Expected 3D or 4D (batch mode) tensor ..."

I wrote a pytorch data loader which used to return data of shape (4,1,192,320) representing the 4 samples of single channel image, each of size ...
Mahesha999's user avatar
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Model Architecture for Time-Series Forecasting with Categorical and Multivariate Data

Context: I was looking at using an LSTM model to forecast the amount of gold gained for each of 10 heroes in a game of Dota 2, a MOBA game, as a base model in some type of model architecture. The game ...
DCRA's user avatar
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Connecting Flatten layer to Dense layer

I'm struggling with my neural network. In short, I need to recreate a model from anywhere on the internet, I've found a model that combines BiLSTM, LSTM and GRU. However, based on the error I got when ...
Tatiana Budanova's user avatar
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Is it possible to calculate a GRU RNN in its entirety by hand on a small dataset?

I want to see whether my code works and compare it to the results I do myself
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What I do wrong with my speech recognition CTC model

I want to train an english speech to text model using architecture similar to deepspeech. In general it has 4 blocks: feature extraction I used melspectrogram. (I used n_mels=80) This translates (...
Захар Наумець's user avatar
1 vote
1 answer
32 views

Handle multiple categorial features in character level RNN

I am working on a fantasy name generator and I have 2 auxiliary categorical features (gender and race). I initially tried concatenating their one hot tensors directly into the input tensor (I think it'...
Shubham Patel's user avatar
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26 views

Using simple RNN to identify a simple dynamic linear system

I have been trying to identify a simple linear second order system (e.g. a pendulum or a mass-spring system), by simulating it in Python using backwards-euler method and then feeding the step changes ...
APasagic's user avatar
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Does a RNN also need a 3-dimensional input vector for a "Point-To-Point" forecast?

I know that for many applications a RNN (e.g. LSTM) needs a 3-dimensional input structure with [Batchsize, Sequence_Length, Features]. My question is if you also need a 3-dimensional input vector when ...
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Predict best chess move using RNNs

I am trying to do a project with AI: in which during any certain moment of a chess game i can predict, using a RNN trained on a kaggle dataset, the best possible move that i can make. I am having ...
user3253067's user avatar
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Is it possible to determine the probability of each time sample to belong a certain class using gaussian distribution with Recurrent Neural Networks?

I'm trying to train a deep learning model that predicts the probability of each time sample in a two-component time series . In this case, I want the target tensor (Y) to be a probability value for ...
Kevin Vargas's user avatar
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Tensorflow RNN - implementing recursive layer

I am dealing with a regression problem, for which I wanted to try to use a recurrent neural network. The general setting is that I have to predict a continuous quantity starting from the evolution, in ...
ChristianC's user avatar
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2 answers
44 views

Binary classification using RNN not going beyond 50% accuracy

I am trying to find out the reason behind why my RNN network won't go beyond 50% for binary classification. My input data is of the shape: ...
Prabhjot Singh Rai's user avatar
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1 answer
130 views

Converting a Standard LSTM RNN over to a Transformer Model

I am looking for some advice on converting my existing CNN/LSTM RNN over to a Transformer type model. This regression model takes a sliding window size of 240 rows with 33 features. It aims to ...
Ted Wilmont's user avatar
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Improving Wake-Word Detection Model Performance: Seeking Advice and Suggestions

I was assigned a task to train a wake-word detection model. Basically, it's a binary sequence classification model on audio samples where it should be 1 if it recognizes the wake word being said (e.g. ...
Ícaro Lorran's user avatar
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Recurrent neural networks (RNN) and popular recurrence relationships

I am looking for a clean problem to study learning process of a RNN. I am thinking of using some number sequences that appear in mathematics and physics, like Fibonacci's, recurrence relations between ...
D Caden's user avatar
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Has someone designed a neural network which can select its own activation functions and/or have multiple activation functions in one model?

I'm wonder if there are any papers or implementations where a neural network has multiple activation functions in a single model (and layer), and preferably also where such activation functions ...
BigMistake's user avatar
2 votes
1 answer
262 views

Role of stateful parameter vs shuffle parameter in LSTM keras

I'm trying to make prediction on a multivariate time series using LSTM. I know stateful=True in keras LSTM means state(hidden) of each sequence, in a batch, at index i - is passed to the next batch, ...
the_he_man's user avatar
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81 views

Is timeseries forecasting for the next timeslot with a RNN a "Many-To-One" type application?

you often find applications that divide RNN according to their input and output data into the categories: One-To-One One-To-Many Many-To-One Many-To-Many as you can see e.g. (here https://...
PeterBe's user avatar
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Math Behind Additive Bahdanau Attention

I am new to NLP field and wanted to apply attention model in one of my projects. I have LSTM model to train, and concatenate some external data sources though attention mechanism. The hidden state ...
user154214's user avatar
1 vote
1 answer
55 views

Why my validation loss and accuracy decays over epochs?

Im trying to build 2 simple networks with cleaned dataset for tweets sentiment classification(0/1): one with all dense layers(binary bag of words) another with RNN layer(embedding layer). But it both ...
emily 's user avatar
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Is this problem a time series regression or seq2seq regression or some other type of problem?

I measure sequences of 3 parameters in my system. 2 are independent and the 3rd dependent. Let's call the independent ones $x$ and $y$, and the dependent one $z$. They are each measured once per hour ...
Hitanshu Sachania's user avatar
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Sending rolling statistics to RNN

I'm curious if anyone has seen cases where sending rolling statistics such as mean, median, min, max, standard deviation, skewness, kurtosis, etc. have been helpful for model accuracy? If so please ...
noNameTed's user avatar
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Forecasting with exogenous variables

I have the following time-series data: ...
Louis GRIMALDI's user avatar
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Matrix time-series Forecasting with LSTM

I have the following time-series data: ...
Louis GRIMALDI's user avatar
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27 views

Detecting Anomalies Using LSTMs

I'm studying this article. The authors used a two-step approach to detect anomalies. First, they used an LSTM to learn the normal behavior of the data. Then, they used the dynamic error thresholds to ...
Vahid Shams's user avatar
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1 answer
59 views

Are there any R packages that support Deep RNNs?

I recently found an interesting paper on what it really means for a recurrent neural network (RNN) to be deep here. Depth can be added in several different ways (state to state, input to state, etc.) ...
noNameTed's user avatar
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41 views

Am I using a network that is too simple for the dataset/task?

I am training an RNN to classify some high-frequency financial data. A very good performance on this data would be an accuracy of >52% or so. I have around 650K training examples and 150K dev set ...
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Finding a template RNN for time series analysis

I would like to create a RNN, that uses one (A) or several time series (with the same length, A, B, C...) as an input and creates another time series (Z) as an output from that . Basically all time ...
xlaub's user avatar
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Error in seq2seq translation when passing predicted output to rnn due to input shape not always being the same

I'm working on a language translator and I'm getting an error I'm unsure about. During the decoding process when using argmax on the predicted output I am sometimes getting an RuntimeError ...
bailey.bailey's user avatar
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Use unclassified texts to improve BERT and RNN GRUN token classification model

I have a training (gold) labelled dataset than consists of 10000 sentences. The task is to create a model that classifies correctly unseen data with B-I-O tags. I have used a BERT and a GRU RNN model. ...
Spyros Triantsfyllou's user avatar
1 vote
0 answers
37 views

ML Modeling Recommendation for Predicting Snake Encounters in Historical Journey Data

I have a dataset consisting of historical journey data where individuals travel from point A to point B. During their journeys, they may encounter varying numbers of animal sightings, including snakes....
Sita's user avatar
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1 vote
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LSTM for classification

I am new to neural networks and I want to use LSTM to classify the on/off state of devices based on power values. In my training dataset, I have power values, device one (0,1), and device 2 (0,1). 0 ...
Zain's user avatar
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1 vote
1 answer
144 views

Which preprocessing is the correct way to forecast time-series data using LSTM?

I just started to study time-series forecasting using RNN. I have a few months of time series data that was an hour unit. The data is a kind of percentage value of my little experiment and no other ...
orde.r's user avatar
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0 votes
1 answer
919 views

How to Implement padding and masking sequences for RNN

As an exercise, I'm building a network for binary classification of sequences (whether a sequence belongs to type A or type B). The network consists of an RNN with one LSTM layer, and on top of it an ...
kodkod's user avatar
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1 vote
2 answers
2k views

RNN with PyTorch - I don't understand the initial parameters

I would like to understand the pyTorch RNN module in detail. There I created a very simple and basic example: ...
Thomas K's user avatar
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0 answers
13 views

RNN Time Series Footfall - How do I construct this RNN?

I have daily time-series data, which tells me the rain fall & foot fall at a certain shop on that day. Now, I want to predict the foot fall at time $t$, given the previous $2$ observations. As I'm ...
the man's user avatar
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1 answer
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Confusion regarding what constitutes a feature in a LSTM?

I have a Time Series problem, where I am trying to predict a single output at time $t$, $y_t$, given the $2$ previous time steps; $X_{t-2}, X_{t-1}$. Let's just look at one observation for simplicity. ...
the man's user avatar
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1 vote
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631 views

Understanding batch size, sequence, sequence length and batch length of a RNN

My Problem I'm struggling with the different definitions of batch size, sequence, sequence length and batch length of a RNN and how to use it in the correct way. First things first - let's clarify the ...
Thomas K's user avatar
5 votes
2 answers
338 views

Modeling uncertainty from known physics

I have an equation given by: $$ \frac{\mathrm{d} s}{\mathrm{d} t}=4a−2s+\lambda(s) $$ where, $a$ is an input constant and $\lambda$ is a non-linear term that depends on $s$. I know that the true ...
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0 votes
1 answer
237 views

How many parameter in an RNN?

I came across this question asking about the number of parameters in an RNN layer, from my understanding it is the number of weights and biases, which in this case is five. Can someone confirm this? ...
user149915's user avatar
0 votes
1 answer
755 views

How to make an RNN model in PyTorch that has a custom hidden layer(s) and that is compatible with PackedSequence

I want to make an RNN that has for example more hidden layers or layer normalization. I know that is it possible to make a custom RNN by subclassing nn.module, but with this approach is it not ...
Philip T 2007's user avatar
4 votes
1 answer
303 views

Working with time series data with several times stamps on a dates, and implementing machine learning

I'm trying to implement predictive analytics on a production data. my goal is to predict next downtime, it's reason and issues. My data looks like below; ...
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