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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Tensorflow data API: Building continuous streams of data from a Dataset of Datasets

I'm trying to build a language model with LSTMs (like ELMO). I've got a lot of documents and want to split them into words as input, but keep their order. So it should get all words of the first ...
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LSTM features classification output

I am very new at this, so I might be wrong about my choice of model, but my problem is the following. I am trying to generate music, hence the reason I am using an LSTM. I have the following sequence ...
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LSTM: many to one and many to many in time-series prediction

I am trying to predict the trajectory of an object over time using LSTM. I have three different configurations of training and predicting values in my mind and I would like to know what the best ...
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What are the advantages of combining BiLSTM and CRF?

BiLSTM-CRF is a common model for sequence tagging (POS tagging, NER, ect.). What are the advantages of combining BiLSTM and CRF? What is the role of each one of the parts in this combination?
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Long sequence prediction with model trained on short sequence

I'll start with a specific example. I would like to train model which predict vector of [0-1]. Values close to 1 on specific range indicates that in those timesteps is particular activation word (...
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Input Shape Problem with Ragged Tensor input in RNN

I have created ragged tensors from python lists as below: list_a=tf.ragged.constant(list_a) list_b=tf.ragged.constant(list_b) Checking the shapes gives me: <...
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How to identify word in a sentence representing the song genre?

I am training a model to identify a word that represents a song genre given a sentence. For example, the model is given a sentence "Beethoven songs are part of the classical genre." The model will ...
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22 views

Input data of variable length to an LSTM

I have a matrix as an input to my LSTM. I want to use a LSTM because the length of the matrix is variable and the width its a fixed size. I would like to know if it is the best option to set a size ...
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time series forecasting of time to leave for multiple customers using one model

I am a beginner in the domain of forecasting and I was wondering if such a problem could be solved with time series analysis : given customer historical data of taxi pickups,along with the weather ...
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Is there any multivariate time series prediction tutorial/code using RNN LSTM for R?

I have been searching and reading many articles, tutorials and academic journals including this forum. All I find is predicting a univariate time series. (except for François Chollet's tutorial, I ...
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Can we use RNN or LSTM for prediction and not forecast

I know RNN and LSTM learn from past data, and can forecast next data. In my situation, I have a learning data-set that hide other information I wish to discover or approximate.(This seems rather an ...
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Keras : Classifying names per origin using RNN and an embedding layer

I am trying to classify names with character RNN using embedding (similar to the PyTorch example https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html) The problem is that ...
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How to train LSTM for multiple time series with multiple variable and diferent size of time series?

I have a dataset of aircraft messages wich have an column that identify each aircraft example: idaircraft=1 , timestamp=340503404, altitude=xxxxxx,longitude = xxxxx, latitude = xxxxx, Touchdown = ...
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LSTM data preparation with multiple independent observation runs

I'm struggling to wrap my head around how to correctly deal with multiple independent time series observations for training an LSTM. If I simply concatenate my observation runs, then there will be an ...
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Increase dimension of RNN LSTM cell in Keras

I want to increase amount of recurrent weights in rnn or lstm cell. The idea is that RNN neuron takes prevois output as input. I want to increase amount of previous values taken as input. If you ...
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Which input to use when generating a new sequence

I want to use sequence-to-sequence architecture to generate sequences. My data has such structure ...
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RNN in pseudo-code

A few years ago, I understood the classical MLP neural network much better when I wrote an implementation from scratch (using only Python + Numpy, without using tensorflow). Now I'd like to do the ...
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Can I use batch normalization and layer normalization both to CNN-RNN model?

I'm performing a classification task with time series data. Therefore, I designed an 1DCNN-LSTM model. Currently, 1d-batch normalization layers are applied for CNN part, but I'm not sure to use layer ...
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How do Bahdanau - Luong Attentions use Query, Value, Key vectors?

In the latest TensorFlow 2.1, the tensorflow.keras.layers submodule contains AdditiveAttention() and ...
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A question with concatenating data

Quite new to deep learning. I'm attempting to use sentiment analysis to examine some word input along with a network to examine some sequential data. I've seen some examples of sentiment analysis, ...
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Language translation with convolutional neural network

Many examples of language translation neural networks: "the cat sat on the mat" -> [model] -> "le chat etait assis sur le tapis" use RNN, and in particular LSTM. See for example Sentences language ...
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Reinforcement learning in bidirectional RNN

I have been self-learning deep generative neural network for a while. I am okay with the basics but I really need some guidance and jump start. I have recently came across this paper “Bidirectional ...
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preprocessing time sequence

I have a long list of event (400 unique events, sequence ~10M long). I want to train an RNN to predict next event. The preprocessing steps i took are: (1) turning to OneHotEncoding using pandas: <...
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Multiple GRU layers in TensorFlow 2

I have some TensorFlow 1 code which implements a GRU layer, and I am updating it to TensorFlow 2. So instead of the hand-written layer iterating over timesteps with a GRUCell I am using the in-built ...
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Classification model using RNN(action detection)

1) Could it be useful to use RNN for classification problem?(e.g. to distinguish which action is taken: car is going, walking, digging, nothing). If 1 question is positive, how should RNN structure ...
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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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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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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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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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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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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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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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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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170 views

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
31 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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