Questions tagged [recurrent-neural-net]

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Are activation's of RNN's hidden states stored while training…if yes, why?

I am trying to understand whether the activation's of hidden states in RNN are stored while learning process, and are used while inference or we only used the learned parameters W,b. I think we can ...
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10 views

Speed Regulation of fan using Machine Learning

Can machine learning be used for the speed regulation of fan based on the environment, how many people are present in the room and routine of a particular individual and how? How can i achieve this?
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Forecast on timeseries using known feature-values in the future

I've setup and trained an rnn-network based model on historical timeseries with 10 features. Note that I am using keras as the framework. I am happy with the results and am already using it to ...
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34 views

Do timesteps must have the same temporal distance in training a RNN?

I have a recurrent neural network with LSTM units that I want to train with batches of 6 timesteps. Each timestep is a record of a dataset and represents the temporal aggregation over 5 minutes of ...
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43 views

How to combine data having similar distribution?

I have a collection of time series data with data points of around 2 years of daily data. I am thinking of a way to increase the number of data points in it so that the neural network gets a better ...
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10 views

Training Recurrent Neural Networks with multiple time series

I have two time series, each one is a bank loan history. The rate, amount and unemployment are features. Rate and amount correspond to the loan, and unemployment is a macroeconomic variable. The ...
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Confusion in RNN terminologies?

I recently started working on RNN and LSTM and got confused with the terminologies. What is the meaning of RNN layer ? When I say RNN has 2 layer - does that mean Two RNN cell connected ...
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31 views

LSTM architecture for multivariate time series

For a multivariate time series analysis, which of the following LSTM architectures would work better and why? 1) Having two independent LSTM layers (one for the time series variable and one for the ...
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1answer
27 views

how bidirectional neural networks can be applied on time series while we do not know the future data?

I have read about bidirectional neural networks. It seems that they need input from both past and future. so lets say we are going to predict the energy use of one hour ahead having the energy use of ...
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17 views

How to use LSTM for time series data?

I've an ECG data spread over time. The duration for each data is around 3 minutes (approx 180 seconds). Each second around 200 recordings were taken. So total length for each sample is approx 36000. ...
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23 views

Contextual Spell Correction

I want to create a spell checker that corrects the spelling mistakes contextually. For example, Erroneous sentence: I want to apply for credit cart Corrected sentence: I want to apply for credit ...
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38 views

How to implement an LSTM RNN with multiple input features

EDIT: Now I didn't convert to list. I am training LSTM for multiple time-series in an array which has a structure: 450x801. There are 450 time series with each of 801 timesteps / time series. The ...
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Changing data structure in incremental learning of LSTM

This is a question which may or may not have open-ended answers. I am curious what you think and hoping to get a starting point. I am wondering what we do if we have a categorical variable in the set, ...
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LSTM training one time step at a time, and accessing prediction after each time step

I have been playing around a bit with keras LSTM but I have some confusion about it's potential applications and how to make use of it. I am new with RNNs so please correct me when I am wrong. Say ...
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2answers
56 views

Trying to understand encoder-decoder sequential models in Keras?

My understanding is that for some types of seq2seq models, you train an encoder and a decoder, and then you set aside the encoder and use only the decoder for the prediction step. For example this ...
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17 views

Confusion with initialising weights in a neural network

Here is some code that initialises weights for an RNN: ...
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23 views

How LSTM can be used to predict action and maximize sales

Hi would like to use LSTM with my dataset. most of people are using LSTM on NLP problem. In my case dataste look like this : IdCustomer | salesMonth_1 | action_1 |salesMonth_2 | action_2 |...
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What is reranking?

I am pretty new to semantic parsing and that stuff but I have to give a presentation about reranking. Can you give a good definition, what reranking does? I think it is related with the parsing ...
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1answer
42 views

What are the exact differences between Deep Learning, Deep Neural Networks, Artificial Neural Networks and further terms?

After having read some theory I am getting a bit confused about the following terms: Deep Learning Deep Neural Network Artificial Neural Network Feedforward Neural Network So, what seems clear to me ...
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9 views

Why/when is someone resetting a recurrent neural network necessary?

This is my first time implementing a recurrent neural network, and I'm confused as to why resetting the node activations is necessary. When is it necessary to reset node activations? Specifically, ...
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1answer
42 views

What is the best way Reinforcement learning, RNN or others to predict the best action we have to take to maximize sales?

I have a dataset composed of few features : customerId, actionDay1, SalesDay1, actionDay20, SalesDay20, actionDay30, SalesDay30 action can be : call email face ...
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How calculate computation time for each part of the network

I want to report how much times it takes to compute each specific part of the network in a batch (forward and backward time). For example, in this paper they've reported RNN, softmax, and optimization ...
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41 views

2019 - Bleeding edge Reinforcement Learning techniques?

I've built an RL agent using the following: Full Rainbow: Double Q-Learning (allow target network to rate the Q-score of the action selected by online network, use this score as a TD target) ...
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20 views

Time Series Forecasting with RNNs

I'm attempting to develop a recurrent model to forecast the value one step into the future (i.e., $x_{t+1}$), given its history $(x_{t-h},\cdots,x_{t})$, where $h$ is a fixed hyperparameter for the ...
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Is Elmo equivalent to Fasttext+Bi-directional GRU?

From what I have read, Elmo uses bi-directional LSTM layers to give contextual embeddings for words in a sentence. So if I use a bi-directional LSTM/GRU layer over Fasttext representations of words, ...
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Difference between globalmaxpoolin1d() and attention layer

What's the difference between globalmaxpoolin1d() and attention layer?
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3answers
59 views

How to know when to stop trainning a deep network?

I've been training several auto encoders containing two GRUs as encoder and decoder during last year. It occurred to me that ...
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39 views

In an RNN, if the gradients don't vanish for long/distant terms, won't the derivative of the error be either divergent to infinity or oscillatory?

P.S. Crosss posted here- https://stats.stackexchange.com/questions/413843/in-an-rnn-if-the-gradients-dont-vanish-for-long-distant-terms-wont-the-deriv, as I've got no answer, I'm asking here: In my ...
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Generalization of RNN/LSTM/GRU… model

Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this: to_predict (orange) was fed to the model, predicted (purple) is the ...
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38 views

TensorFlow: how to restore pre-trained meta model and pass it's weights and biases to the optimizer?

I trained a model on a specific dataset and saved it as a meta, I want to restore the model and use its weights and biases on another dataset the code isn't mine but I'm trying to restore the ...
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12 views

How can memory networks perform well in lists/set type?

I was reading this paper about memory networks. As I understood, memory networks can give output in a word. But on Babi dataset's 'list/set' task, its accuracy was almost 80%. What have I ...
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28 views

Does an LSTM model with one hidden layer has much advantages over a RNN or NN?

Does an LSTM model with one hidden layer has much advantages over a RNN or NN? Cause the network is not deep/large
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1answer
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How can I train a many-to-one RNN with an array of 2D matrices?

I have eye tracking data for every word of a novel. Features for every word is given separately. I want to take groups of 100 words to make a sample and then use each of these samples as a single ...
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Why are reservoir computer so useful for hardware implementations

I often read (e.g. here or in this question) that Reservoir Computer (RC) are useful in the field of Neuromorphic Computing where they can serve as efficient implementations of neural networks in ...
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What will go wrong if we apply linear or other types of regression to translate sentences between two languages?

Disclaimer: I asked the question at https://stats.stackexchange.com/questions/408463/what-will-go-wrong-if-we-apply-linear-or-other-types-of-regression-to-translate, but didn't get any response, so I'...
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What's the difference between hidden layer size and sequence length in RNN and LSTM?

I have been exploring RNNs in keras implementations. In the LSTM layer we have to provide a hidden layer size and also a sequence length. My question is, what does hidden layer size correspond to and ...
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99 views

How to apply an RNN to forecast non-stationary time series?

Is it possible to predict a time series which is non-stationary, in the sense that, the dependent variable Y have an increasing trend? Therefore, the highest value of $Y$ in the training set may be ...
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1answer
50 views

LSTM input and output for sentiment analysis

I'm studying this LSTM network: https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis ...
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1answer
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Why don't we gradually update the activation parameters in RNN from one activation to the next as the network is learning more?

I'm very new to (unidirectional, vanilla) RNN and sequence modeling in general, and all I understood about the motivation on having the connection between two successive hidden layers/activation is ...
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82 views

LSTM Predict values out of test

I'm trying to predic stock values from a dataset, for example: Google stock. I have this easy model. ...
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1answer
28 views

How to perform polynomial landmark detection with deep learning

I am trying to build a system to segment vehicles using a deep convolutional neural network. I am familiar with predicting a set amount of points (i.e. ending a neural architecture with a Dense layer ...
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18 views

Strategy for “forcing” number of labels in seq2seq predictions with Keras?

I'm trying to train a seq2seq model that for every timestep in a given timeseries sample will output 1 of 6 possible labels. Furthermore, the training data is constructed in such a way that Each ...
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210 views

One hot encoding as input to recurrent neural networks

I'm trying to predict next label in a pattern based on previous labels using recurrent neural network. In total I have 100 labels Example of input pattern: ...
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100 views

Using an RNN to predict fantasy football results

So I have a couple questions about the design of a neural network. I'm trying to create a neural network to predict the number of fantasy points a player will score in a given week. First of all, I ...
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82 views

Sequence classification using oneClass SVM

In the code below, I'm using a sequence to sequence approach as a prediction model for anomaly detection. The data set I'm working with is ADFA-LD. The training phase is done using only normal ...
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1answer
140 views

How to feed a table per timestamp to LSTM neural network?

I have a time-series dataframe like this ...
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29 views

What encoding to use for my musical vectors?

I'm trying to build a music recommendations system using an encoder-decoder sequence-to-sequence architecture using keras. My dataset comprises of playlists containing songs represented as a 13-...
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1answer
37 views

Working ofLSTM with multiple Units - NER

I am trying to understand working of LSTM networks and kind of not clear about how different neurons in a cell interact each other. I had a look at a similar question, but still not clear about few ...
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Adding context in a sequence to sequence problem

The encoder of a seq2seq model is meant to generate a conditioning context for the decoder, as mentioned here A RNN layer (or stack thereof) acts as "encoder": it processes the input sequence and ...
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Why is MLP working similar to RNN for text generation

I was trying to perform text generation using only a character level feed-forward neural network after having followed this tutorial which uses LSTM. I one-hot encoded the characters of my corpus ...