Implementing Backpropagation in LSTM Networks

Implementing Backpropagation in LSTM Networks

Table of Contents:

1. Introduction
2. Implementing Forward Pass 2.1. Using Geeks for Geeks as a Reference 2.2. Aligning Labels
3. Implementing Backpropagation 3.1. Adding Softmax Activation 3.2. Dealing with Typos and Accuracy 3.3. Calculating Gradients
4. Updating Weights
5. Handling Forget Gates
6. Understanding Matrix Multiplication
7. Managing Bias
8. Rolling Back the Deltas
9. Reviewing Progress
10. Conclusion

Introduction

In this article, we will Delve into the world of implementing backpropagation in neural networks, specifically an LSTM network. We will go step by step, covering the necessary concepts and techniques to effectively perform backpropagation and update the weights of the network. So, let's get started!

Implementing Forward Pass

To begin, let's first understand how to implement the forward pass in our LSTM network. We will be using Geeks for Geeks as a reference, as it provides a comprehensive and easy-to-understand explanation of the forward pass. By aligning our labels accordingly, we ensure that our implementation matches the approach outlined in the article.

Implementing Backpropagation

Now that we have successfully implemented the forward pass, it's time to tackle the backpropagation process. One important question that arises is whether we need to Apply a softmax activation during backpropagation. We will experiment and observe the impact on our results. However, it's essential to be cautious while implementing backpropagation due to potential typos and inaccuracies in the code.

Updating Weights

Once the backpropagation process is correctly implemented, we can move on to the crucial step of updating the weights. By taking the gradients with respect to the forget gates, we can ensure that our weight updates are accurate and aligned with the desired outcomes. We Are now getting closer to the end goal.

Handling Forget Gates

The forget gates play a crucial role in the LSTM network. To ensure the completeness of our implementation, we must address the forget gates separately. By properly handling the gradients related to the forget gates, we can ensure the effectiveness of the backpropagation process.

Understanding Matrix Multiplication

During the implementation, it is important to understand the different types of matrix multiplication: element-wise multiplication and standard matrix multiplication. This distinction can impact the compatibility of matrices and the accuracy of our results. Let's explore when and how to apply each type of matrix multiplication.

Managing Bias

The bias is a significant component of LSTM networks. We need to ensure that we handle the bias correctly during backpropagation. As we update the weights, it is crucial to understand the Dimensions and compatibility of the matrices involved to ensure accurate bias calculation.

Rolling Back the Deltas

As we progress through the backpropagation process, we need to Roll back the deltas and consistently add them to each other. By following this approach, we can ensure that the deltas are appropriately incorporated into the weight updates. Let's explore this rolling back technique in more Detail.

Reviewing Progress

At this stage, we have made significant headway in implementing both the forward pass and backpropagation. It is essential to review our progress so far, ensuring accuracy and correctness in the code. This review allows us to make any necessary adjustments and fine-tune our LSTM network implementation.

Conclusion

In conclusion, we have covered various aspects of implementing backpropagation in LSTM networks. We have discussed the forward pass, backpropagation process, weight updates, management of forget gates, matrix multiplication, bias handling, and rolling back the deltas. By following these steps, we are well-equipped to train our first LSTM network successfully.