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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How can I do a sequence to sequence model (RNN / LSTM) with Keras with fixed length data?

What I'm trying to do seems so simple, but I can't find any examples online. First, I'm not working in language, so all of the embedding stuff adds needless ...
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29 views

What is the reason to use a RNN over a CNN in text classification task? [closed]

When should i switch to using an RNN (LSTM, GRU) over a simple CNN for classifying web text articles on pre-specified taxonomy? My input is 100K of news feed articles mostly pre-labeled in 15 ...
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How can I create a basic RNN for audio with PyTorch?

I am trying to have my RNN learn to take corrupted audio files and clean them. To that end, I see that I need to convert to MFCC and use that instead of raw time ...
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17 views

Sequence classification with highly irregular time series

I'm trying to predict whether a sequence of events contains or will contain a specific event type, with labels being a binary yes or no to the specific event type occurring in that sequence. My data ...
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Time Series Forecasting with RNN/LSTM/NARX

I have some experimental datasets (like 4 or 5), and each dataset has three time series data, say $u1(t)$, $u2(t)$, and $x(t)$. The three time series of each experiment are similar but not the same. ...
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Use LSTM to predict the proportion of steps with nonzero feature values

I am trying to do a simple regression for sequences. Each input $X_i$ is a $n=2000$ by 1 matrix, formatted as $n_i$ 0-s followed by $(n-n_i)$ 1-s. The output $y_i$ should be $n_i/n$, i.e. the ...
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27 views

What is the output of multivariate LSTM model?

I am currently trying to build an LSTM model by using multivariate inputs, but I don't understand what exact output I am predicting. I am currently using 5 features in the data i.e. 'Time', 'Avg CPU ...
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How to identify and extract text from table in a image ? Are there any machine learning model avaible for extracting text in a table?

How to identify and extract text from table in a image ? sample image shown below Are there any machine learning model available for identifying table and extracting text in a table ? i tried ...
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29 views

Loss is decreasing correctly but upon prediction, totally wrong results

I've made a pretty basic stock prediction RNN with it's only input being the past stock price from apple. On this case, I want to input the apple stock price and the samsung stock price (2 features). ...
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35 views

Loss being outputed as nan in keras RNN

Since the first Epoch of the RNN, the loss value is being outputted as nan. Epoch 1/100 9787/9787 [==============================] - 22s 2ms/step - loss: nan I have normalized the data. ...
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Implementing an RNN on multiple text sources

I want to implement an RNN to generate a new text based on many examples of existing texts of a certain format in the training data. The type of texts in the training data consists of 3 segments, ...
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29 views

Keras RNN (batch_size

I created RNN model for text classification with LSTM layer, but when I put the batch_size in the fit method, my model trained on the whole batch instead of just the mini batch _size. This also ...
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h in LSTM increasing in size?

So I was reading about the LSTM architecture and I was having trouble understanding a certain aspect of it. This article mentions the step in question near the bottom of the page. Here is the image ...
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What is the role of $W_{ax}, W_{aa}, W_{ay}$ in forward propagation in RNN? Are they hyperparameters? Why are they needed?

In RNN introduction in Coursera sequence model course, the following formula for forward propagation in RNN was introduced. What exactly is the role of $W_{ax}, W_{aa}, W_{ay}$? What do they do? In ...
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Metric to evaluate words generated by Neural Network

I have this task at hand and I would be grateful for some directions. Perfectly not the final solution as I would like to do it myself. Let's say I need to create new fruit names based on existing ...
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Training time-series regression RNN's

I'm looking for references on training time-series regression RNN models. For learning purposes I want to implement myself using autograd (or JAX) rather than a high level library. I cannot find ...
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35 views

how to apply feature selection on LSTM-RNN? [closed]

am doing my research using lstm-rnn algorithm. i have time-series and non time-series features. how to apply lstm on my dataset? and also how to apply feature selection mechanism to select features?
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Proper loss function for sequence prediction model with multi-step output

Consider a typical time series (sequence) prediction problem that use previous $k$ step historical features to predict the next step target. We use RNN model as an ...
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Is it possible to get prediction intervals in sequenced data RNN forecasting?

Is it possible to get prediction intervals in sequenced data RNN (keras+python)forecasting? For example: predicts your car sales or new purchase The question is: UserId 1 will change his car in the ...
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How to test yolo model with correct .name configuration file as it is getting pointed to data/coco.names instead of cfg/obj.names while testing?

Im getting below error while testing my newly retrained model on my custome dataset command to test : !./darknet detect cfg/yolo-voc.2.0.cfg backup/yolo-voc_last.weights helmet.jpg output: Error: in ...
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About batches in stateful RNN

..., to create proper consecutive batches, where the nth input sequence in a batch starts off exactly where the nth input sequence ended in the previous batch. Géron, Aurélien. Hands-On Machine ...
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TensorFlow / Keras: What is stateful = True in LSTM layers?

Could you elaborate on this argument? I found the brief explanation from the docs unsatisfying: stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will ...
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what is darknet and why is it needed for YOLO object detection?

what is darknet and why is it needed for YOLO object detection ? I read that its a neural network written in C , but why is it needed for YOLO object detection when we have lot of machine learning ...
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Setting up RNN in TensorFlow for time series forecast with variable input series lengths

I am building a model with keras for time series prediction. The structure of the problem is as follows: The input is a time series of 5 numeric features The ...
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1answer
30 views

About seq2seq networks

I have read that seq2seq is a network, similar to other networks types (CNN, RNN, ...). However, in my opinion it is actually an architecture for RNNs. Isn't that? For example, when input and output ...
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Why cant RNN learn long term dependencies=?

In Colah's blog, he explain this. In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of ...
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1answer
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How does backpropagation work with averaging layers?

I'm studying Word2Vec algorithm, and so far i understood that, in the case of input context bigger than 1 (so multiple words) we have our hidden layer that performs averaging between the inputs (as ...
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1answer
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should I shift a dataset to use it for Time series regression with RNN/LSTM?

I'm seeing this tutorial to know how to use LSTM to predict time series data and I noticed that he shifted the target/labels up so that the features are all in time t but the target is t+1 so my ...
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what are the step need to be followed inorder to retrain any pretrained neural network model?

what are the step need to be followed inorder to retrain any pretrained neural network model ? (How to load the pre-trained BERT model from local/colab directory? ) I tried to re train few pretrained ...
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What Non-linearities are best in Denoising RNN Autoencoders and where should the go?

I’m employing a denoising RNN autoencoder for a project relating to motion capture data. This is my first time using auto encoder architectures and I was just wondering what non-linearities should be ...
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1answer
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Why RNNs necessary for time series?

I got it that when using time series data, I have to use a RNN The highlight here is that the neurons receive their output again as an input, so they can take into account the previous step. But ...
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k-fold cross validation with RNNs

is it a good idea to use k-fold cross-validation in the recurrent neural network (RNN) to alleviate overfitting? A potential solution could be ...
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Tensorflow2, Bilateral LTSM 'NoneType' object has no attribute 'outer_context'

The following model created in Tensorfow2 and Keras: ...
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How do I disable libtorch warning

Recently I deployed a program using libtorch (PyTorch C++ API). The program run as expected but its gives me a warning. ...
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1answer
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In Deep Learning, how many kinds of Attention exist? And what is the history of Attention models? [closed]

How many definitions of attention are commonly employed for Deep Learning tasks? That's what I've encountered up to now: Self-attention Bahdanau Luong Multi-Head (used in Transformers) Could you ...
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1answer
64 views

Keras LSTM Input Shape - Batch Size and Time Step

So I have 82 different sets of data, each with varying length where each point has one feature and a label (0 or 1). I'm trying to use Keras LSTM to be able to predict the class of a point depending ...
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why an advanced LSTM model produce the same results as a simpler one?

I have implemented the model proposed in this article which is a text classification model that uses sentence representation rather than only word representation to classify texts. ...
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Convert tensor containing label to tensor containing one hot

Context I'm building a vanilla RNN with Tensorflow and I'm inputting my labels via the following placeholder: ...
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Multiple merging multiple convolutions

(First post here) I am rather new to neural networks, having used Tensorflow for a couple months now, and am looking for some advice I have on an idea to improve the accuracy of my model. I am looking ...
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back propagation through time derivation issue

I read several posts about BPTT for RNN, but I am actually a bit confused about one step in the derivation. Given $$h_t=f(b+Wh_{t-1}+Ux_t)$$ when we compute $\frac{\partial h_t}{\partial W}$, does ...
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Improving Performance of LSTM for time series prediction

My data consists of two features and a set of time series data labeled as "bookings". I have 1056 data point in the times series, for which I have two features for each. The data size is 1056x3. My ...
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1answer
36 views

How to scale a variable when not knowing the maximum

I have a dataset with different features where some of them are not categorical, so they need to be scaled or normalized (especially the target). However, normalizing between 0-1 for instance means ...
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Effective Time Series Forecasting using Keras/LSTM

I am working on time series forecasting for an engineering component (turbo charger). I have dataset containing field data from sensors (=features) taken every day for different turbocharger for their ...
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1answer
22 views

Intuition behind the RNN/LSTM hidden state?

What's the intuition behind the hidden states of RNN/LSTM? Are they similar to the hidden states of HMM (Hidden Markov Model)? Thanks!
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1answer
73 views

What model should I use for multiple time series input

I want to predict bacteria plate count in the water from time series(around 10000 values in a row) of water temperature on a one minute granularity, and other daily climate data including min and max ...
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is it possible to implement LSTM with input shape (sample,timestep,timestep,feature)?

I'm new to Keras. I am trying to implement this model https://www.aclweb.org/anthology/D15-1167 for document classification, and I want to use LSTM for getting sentence representation. I have trained ...
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Static and Dynamic neural networks process

Neural networks can be classified into static (convention feedforward networks) and dynamic categories (RNN, LSTMs). ...
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Structure of LSTM gates

It is my impression that a single layer LSTM architecture consists of $t$ LSTM cells that are identical duplicates, where $t$ is the number of time steps. Then there are gates within the LSTM cell. I ...
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Machine learning algorithm for classifying a 2xN array of ranged coordinates?

Good afternoon, I have a dataset of lists of coordinates that are ranged from (0, 100) on the Y-axis and (0, 300) on the x-axis, with double precision. I'm looking into classifier algorithms that ...
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Accelerometer and Gyroscope features

I am having accelerometer and gyroscope reading along x,y,z axis and want to get motion direction info at each time step. What all feature extraction would be best suited for this type of requirement. ...

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