This repository contains a PyTorch implementation of a simple neural network model that includes learnable positional encodings. The model is designed to demonstrate how positional information can be dynamically learned within sequence data, potentially improving the model's understanding and predictive capabilities for tasks where the order of data points is significant.
model_and_train.py- A single Python script containing the dataset preparation, model definition, learnable positional encoding, and training loop.
To run this project, ensure that you have Python and PyTorch installed. You can install PyTorch using the following command: pip install torch
To execute the model training, simply run the following command in your terminal: python model_and_train.py This command will start the training process and display the loss at the end of each epoch, allowing you to monitor the model's learning progress.
The model consists of the following components integrated into a single Python script:
A custom PyTorch Dataset class that generates random sequences of data. Each sequence has a predefined length with randomly generated features.
A PyTorch nn.Module that adds learnable positional encodings to the input sequences. This feature enables the model to account for the order of data points within each sequence, adapting these encodings during the training process.
Combines the learnable positional encoding module with a basic linear layer. This setup demonstrates a straightforward application of incorporating sequence order awareness into neural network architectures.
The training script included in model_and_train.py sets up the model and data loader, then runs a training loop for a specified number of epochs. It uses Mean Squared Error (MSE) for loss calculation and Adam as the optimizer.