Recurrent Neural Networks (RNNs) are a type of neural network that are commonly used for working with sequential data where the order of the data is important. In RNN, input sequence is processed sequentially, one element at a time, and each element has a hidden state that is shared among all the elements. RNNs suffers from vanishing gradient problem while training over long sequences which hinders its capability to capture long term dependencies. To resolve this issue, two improved RNN variants were proposed: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
LSTM and GRU are designed to deal with the vanishing gradient problem of RNNs by introducing some extra gating mechanisms. These mechanisms enable the LSTM and GRU networks to selectively retain information over time and have been found to be particularly useful for tasks that require modelling long-term dependencies. Here are the key differences between LSTM and GRU layers in PyTorch:
### LSTM LSTM is a type of RNN that has the ability to selectively retain or forget information over time. It was introduced by Hochreiter and Schmidhuber in 1997 [1]. LSTM architectures usually consist of a cell state, which is modified by three different gates:
- Forget Gate: It decides when to forget the information that is no longer required - Input Gate: It decides when to add the new information to the cell state - Output Gate: It decides what information to output based on the current cell state
LSTM networks have shown remarkable performance in language modelling, speech recognition and machine translation.
Here is an example of how to implement LSTM layer in PyTorch:
import torch
import torch.nn as nn
# Input size = 10, Hidden size = 20, Num Layers = 2
lstm = nn.LSTM(input_size=10, hidden_size=20, num_layers=2, batch_first=True)
# Input sequence with 32 elements, each of size 10
inputs = torch.randn(32, 32, 10)
# Hidden state and cell state initialization
h0 = torch.randn(2, 32, 20)
c0 = torch.randn(2, 32, 20)
# Forward pass
output, (hn, cn) = lstm(inputs, (h0, c0))### GRU GRU is a simplified version of LSTM, proposed by Cho et al. in 2014 [2]. It combines the forget and input gates into a single update gate, and uses a reset gate to control the contribution of the past memory.
GRUs have shown to be successful in many NLP tasks such as machine translation and sentence modelling. One of the most significant advantages of GRU is its computational efficiency compared to LSTM.
Here is an example of how to implement GRU layer in PyTorch:
import torch
import torch.nn as nn
# Input size = 10, Hidden size = 20, Num Layers = 1
gru = nn.GRU(input_size=10, hidden_size=20, num_layers=1, batch_first=True)
# Input sequence with 32 elements, each of size 10
inputs = torch.randn(32, 32, 10)
# Hidden state initialization
h0 = torch.randn(1, 32, 20)
# Forward pass
output, hn = gru(inputs, h0)In summary, RNNs are used for processing sequential data, while LSTM and GRU are used for capturing long-term dependencies in sequence data. LSTM networks are typically used in tasks where long term dependencies are expected, such as speech recognition, machine translation, and handwriting recognition. GRU networks are simpler and computationally efficient compared to LSTM, and are often used in applications such as music synthesis and natural language processing.