I'm learning RNN and know that a basic RNN maps an input sequence to a sequence of hidden states, and then maps the sequence of hidden states to the output sequence. I'm confused about the dimension of the 3 sequences. The dimension of the input and output sequence can be found from data, but what about the dimension of the hidden states? Is it set by people in advance, or set to be the dimension of the output or input sequence?

**Contents**hide

#### Best Answer

The dimension of the RNN hidden state can be chosen in advance. It is independent of the dimension of the input or output sequences.

### Similar Posts:

- Solved – What does alignment between input and output mean for recurrent neural network
- Solved – How to stack a convolutional autoencoder
- Solved – How to stack a convolutional autoencoder
- Solved – Training an Elman Recurrent Neural Network
- Solved – Bottleneck building block in Residual learning networks