Questions tagged [gated-recurrent-unit]

For questions related to the gated recurrent unit (GRU), a modification and simplification of the LSTM unit, which is a more sophisticated unit (with respect to the standard one) of a recurrent neural network (RNN). An RNN that uses GRU units is often called a GRU network. GRUs were introduced in the paper "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" (2014) by Kyunghyun Cho et al.

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Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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33 views

Incorporating domain knowledge into recurrent network

I am currently trying to solve a classification task with a recurrent artificial neural network (RNN). Situation There are up to 350 inputs (X) mapped on one categorical output (y)(13 differnt ...
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49 views

What's the difference between LSTM and GRU?

The main difference between these two structures lies in the number of gates and their specific roles. The role of the Update gate in the GRU is very similar to the Input and Forget gates in the LSTM. ...
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1answer
23 views

Inner working of Bidirectional RNNs

I'm trying to understand how Bidirectional RNNs work. Specifically, I want to know whether a single cell is used with different states, or two different cells are used, each having independent ...
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79 views

RNN models displays upper limit on predictions

I have trained a RNN, GRU, and LSTM on the same dataset, and looking at their respective predictions I have observed, that they all display an upper limit on the value they can predict. I have ...
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1answer
354 views

How do I choose the size of the hidden state of a GRU?

I'm trying to understand how the size of the hidden state affects the GRU. For example, suppose I want to make a GRU count. I'm gonna feed it with three numbers, and I expect it to predict the ...
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91 views

Why are GRU and LSTM better than standard RNNs?

It seems that older RNNs have a limitation for their use cases and have been outperformed by other recurrent architectures, such as the LSTM and GRU.