Char-rnn pytorch

1. What is character level RNN?
2. What is RNN Pytorch?
3. Why LSTM is better than RNN?
4. What is the output of RNN Pytorch?

What is character level RNN?

A character-level RNN reads words as a series of characters - outputting a prediction and “hidden state” at each step, feeding its previous hidden state into each next step. We take the final prediction to be the output, i.e. which class the word belongs to.

What is RNN Pytorch?

Basically, Pytorch rnn means Recurrent Neural Network, and it is one type of deep learning which is a sequential algorithm. In deep learning, we know that each input and output of a layer is independent from other layers, so it is called recurrent.

Why LSTM is better than RNN?

LSTM networks combat the RNN's vanishing gradients or long-term dependence issue. Gradient vanishing refers to the loss of information in a neural network as connections recur over a longer period. In simple words, LSTM tackles gradient vanishing by ignoring useless data/information in the network.

What is the output of RNN Pytorch?

RNN has two outputs - out and hidden . out is the output of the RNN from all timesteps from the last RNN layer. It is of the size (seq_len, batch, num_directions * hidden_size) . If batch_first=True , the output size is (batch, seq_len, num_directions * hidden_size) .