What is GRU in R

This recipe explains what is GRU in R

Recipe Objective - What is GRU in R?

GRU or Gated Recurrent Unit are the improved version of recurrent neural networks(RNN). Gated recurrent unit solves the vanishing gradient problem of recurrent neural networks. GRU uses "update gate and reset gate" technique which involves two vectors which decides what information should be passed to output and most special thing about them is that they can be trained to keep the information from long ago without removing information or washing of it through time which is mostly irrelevant to prediction. Gated recurrent unit outperforms RNN and LSTM due to simpler architecture. Gated recurrent unit control the flow of information through gates just like LSTM but in a simpler way.

This recipe explains what is GRU, how it is beneficial for neural network models and how it can be executed.

Learn to use RNN for Text Classification with Source Code

Explanation of GRU.

The update gate vector operation helps the model to determine that how much of the past information that is from the previous time steps needs to be passed along to future and that is powerful as the model has got the right to decide to copy all information from the past and eliminate risk of vanishing gradient problem.

The reset gate vector operation is used from the neural network model to decide the amount of past information to forget. The difference in update gate and reset gate comes in weights and the gate’s usage

Gated recurrent unit keeps the current memory content which uses weights, Hadamard product, gates operation and non-linear activation function like tanh which helps in calculating h_t vector which holds the information for the current unit and passes it down to neural network which further on applied to element wise multiplication and that finally helps in eliminating vanishing gradient problem as the model is not washing out new input every single time but it keeps the relevant information and passes it down to next time steps of neural network.

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