LSTM pytorch

LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is widely used in natural language processing tasks. In PyTorch, you can implement LSTM models using the `torch.nn.LSTM` module.

Here's an example of how you can create an LSTM model in PyTorch:

import torch
import torch.nn as nn

# Define the LSTM model
class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# Instantiate the LSTM model
input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1

model = LSTMModel(input_size, hidden_size, num_layers, output_size)

In this example, we define an LSTM model with an input size of 10, a hidden size of 20, 2 LSTM layers, and an output size of 1. The `forward` method performs the forward pass of the network.

You can then train this model using your dataset and loss function of choice.

I hope this helps! Let me know if you have any further questions.