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Questions tagged [recurrent-neural-network]

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Why does forecasting with an LSTM yield better results with shuffling?

I first partition the timeseries data into train, validation, and test splits, without performing any shuffling. Each row is a window of ordered samples, so my training data might be shaped ...
MuhammedYunus's user avatar
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Uneven spaced time series data, any advise on how to approach?

In dealing with uneven spaced time series data, any advise what would be the approach ? data is ECG data to predict if the blood pressure Sys would drop -20% or 80% of normal. In the usual approach ...
curiosityfrown's user avatar
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Why is energy jumping and images converge incorrectly in Hopfield network Python implementation?

I'm trying to implement a Hopfield network that denoises images. Here's wikipedia for Hopfield networks that I used as reference: https://en.wikipedia.org/wiki/Hopfield_network I believe my fit2 ...
zxcv's user avatar
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I can't get good performance from BERT

I trained NLP models. This is a subset (200 instances) of my data set of 10,000 instances:This the link of the dataset on pastebin I compare an LSTM model with a glove model and a BERT model. I ...
Seydou GORO's user avatar
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Difference between batch_size in TimeSeriesGenerator and model.fit batch_size

im wondering if there is a difference between the batch_size set in the TimeSeriesGenerator and the batch_size in the model_fit. I create some RNN Forecasts for timeseries. ...
prgtttt's user avatar
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What are the evaluation metrics we can use for RNN models?

I'm working on a few RNN (Recurrent Neural Network) models and want to evaluate those models, so I'm looking for useful metrics to evaluate RNN models?
Abdul Rehman's user avatar
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Does the sequence length of a RNN/LSTM have to be the same for the input and output?

I have a question about the input and output data in a RNN or LSTM. A RNN expects a 3-dimensional vector as input of the form (Batch_size, sequence_length_input, features_input) and a 3-dimensional ...
PeterBe's user avatar
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RCNN to predict sequence of images (video frames)?

In the following work the authors apply a convolutional recurrent neural network (RNN) to predict the spatiotemporal evolution of microstructure represented by 2D image sequences. In particular, they ...
Betelgeuse's user avatar
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Dual Branch Recurrent Neural Network, what is the correct architecture and can I turn off one branch?

Let's suppose I want to predict the daily consumption of apples in the next 30 days of a person and I have, as input, the historical information about the past 60 days and the demographic information ...
oettam_oisolliv's user avatar
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Multivariate RNN/LSTM architecture for optimizing one input variable

Let $x = [x_1, x_2, x_3]$ and $y = y$ where all variables in $x$ correlates highly with $y$ and there could also be some crosscorrelation within the set of variables in $x$. The data takes the form of ...
lewisloathing's user avatar
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Use Lstm for classifying problem

i have a dataset of 10000 event with 16 feature, and a vector of dimension 10000 that represent the label of each event; for what i understand is a classification problem but it's required to use a ...
2330nb111's user avatar
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external input and Reinforcement Learning

Is there an RL method which in it the next state depends on the "current action" and "current state" AND an "External Input"?
Hamed Molaei's user avatar
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Back propagation process of RNN?

I'm learning how to use the Recurrent Neural Network model (RNN). I'm not entirely sure about the feed-forward procedure in RNN. It includes, for example, input, hidden state, and output. As far as I ...
learner's user avatar
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Hopfield Network python implementation, Network doesn't converge to one of the learned patterns

I'm trying to implement a Hopfield Network in python using the NumPy library. The network has 2500 nodes (50 height x 50 width). The network learns 10 patterns from images of size 50x50 stored in &...
Emoticon's user avatar
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How to put marker on time series training set

My input is this picture And I would like to put markers on it and use time series with markers as a label Two picture are not scaled. Main point is I would like to train RNN classifier and let it be ...
ii2's user avatar
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Problems to understand how to create the input data for time series forecasting with a recurrent neural network in Keras

I just started to use recurrent neural networks (RNN) with Keras for time-series forecasting and I found this tutorial Forecasting with RNN. I have difficulties understanding how to build the training ...
PeterBe's user avatar
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