Implementing Long Short Term Memory using PyTorch

For implementing LSTMs using PyTorch, we will following the steps discussed below:

Step 1: Install Necessary Libraries

For this implementation, we will required PyTorch library, that we can install using the following command:

pip install torch torchvision

Step 2: Defining LSTM Model

For defining the LSTM model, we will define an LSTMModel class, which inherits from nn.Module in PyTorch. It includes an LSTM layer followed by a fully connected layer (linear layer) for the final output.

The forward method defines the forward pass of the model, where the input sequence x is passed through the LSTM layer, and the final hidden state is passed through the fully connected layer to produce the output.

The initial hidden state and cell state are initialized as zeros, and the gradients are detached to prevent backpropagation through time.

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTMModel, self).__init__()
        self.hidden_dim = hidden_dim
        self.layer_dim = layer_dim

        # LSTM layer
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)

        # Fully connected layer
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # Initialize hidden state with zeros
        h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()

        # Initialize cell state
        c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_()

        # Detach the gradients to prevent backpropagation through time
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))

        # Reshaping the outputs for the fully connected layer
        out = self.fc(out[:, -1, :])
        return out

Step 3: Model Training

To train the LSTM model, you will typically use a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification, along with an optimizer like Adam:

model = LSTMModel(input_dim=1, hidden_dim=100, layer_dim=1, output_dim=1)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# Training loop
for epoch in range(num_epochs):
    outputs = model(trainX)
    optimizer.zero_grad()
    loss = criterion(outputs, trainY)
    loss.backward()
    optimizer.step()

    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

Long Short-Term Memory Networks (LSTMs) are used for sequential data analysis. LSTM offers solutions to the challenges of learning long-term dependencies. In this article, explore how LSTM works, and how we can build and train LSTM models in PyTorch.