pytorch lsem

PyTorch is a popular deep learning framework that provides various modules and tools for building and training neural networks. The LSTM module in PyTorch is used for implementing Long Short-Term Memory (LSTM) networks, which are a type of recurrent neural network (RNN).

To use LSTM in PyTorch, you first need to import the necessary modules:

import torch
import torch.nn as nn

Then, you can define an LSTM network using the `nn.LSTM` class. Here's a simple example of how to define an LSTM network with one input layer, one LSTM layer, and one output layer:

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

In this example, `input_size` represents the number of features in the input data, `hidden_size` is the number of features in the hidden state of the LSTM, `num_layers` is the number of recurrent layers, and `output_size` is the number of output features.

You can then create an instance of the `LSTMModel` class and use it for training and inference:

input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1

model = LSTMModel(input_size, hidden_size,