Backpropagation Part 1: Mengupdate Bobot Hidden Layer | Machine Learning 101 | Eps 15

Introduction

In this tutorial, we will explore the process of updating weights in the hidden layer of a neural network using backpropagation. This is a crucial concept in machine learning, particularly in training multi-layer perceptrons. Understanding backpropagation allows you to effectively optimize your neural networks, leading to better performance in various tasks.

Step 1: Understand the Basics of Backpropagation

Before diving into the weight update process, it’s essential to grasp the core concepts of backpropagation:

• Backpropagation is an algorithm used for training neural networks by minimizing the error between predicted and actual outputs.
• It involves calculating the gradient of the loss function with respect to each weight by the chain rule.

Practical Tip

Familiarize yourself with terms such as loss function, gradients, and activation functions, as they are integral to understanding how backpropagation works.

Step 2: Prepare Your Neural Network Model

Set up a basic multi-layer perceptron structure:

• Input Layer: Accepts input features.
• Hidden Layer(s): Processes inputs through weights and activation functions.
• Output Layer: Produces the final prediction.

Practical Advice

Choose an appropriate activation function for the hidden layer (e.g., ReLU or sigmoid) to ensure non-linearity in learning.

Step 3: Calculate the Loss Function

Determine how well your model is performing by calculating the loss function. The most common loss functions include:

• Mean Squared Error (MSE) for regression tasks.
• Cross-Entropy Loss for classification tasks.

Implementation

For example, you can use the following formula for MSE:

Loss = (1/n) * Σ(actual - predicted)²

Step 4: Compute Gradients for the Output Layer

Calculate the gradients for the output layer using the derivative of the loss function:

• For MSE, the gradient can be calculated as:

Gradient = (predicted - actual) * activation_derivative(output)

Common Pitfall

Make sure to use the derivative of the activation function applied to the output layer, as this affects the weight updates significantly.

Step 5: Backpropagate to Hidden Layers

Once you have the output layer gradients, propagate the error back to the hidden layers:

• Calculate the gradients for the hidden layer by applying the chain rule.

Hidden Gradient = (output_gradient * weights_hidden_to_output) * activation_derivative(hidden_output)

Step 6: Update Weights

With the gradients calculated, you can now update the weights:

• Use the following formula for updating weights:

new_weight = old_weight - learning_rate * gradient

Practical Tip

Choose an appropriate learning rate. A rate that is too high may cause divergence, while one that is too low can slow down convergence.

Conclusion

In this tutorial, we covered the fundamentals of backpropagation, from understanding the basic concepts to updating the weights in the hidden layers of a neural network. By mastering these steps, you can effectively train your neural networks and improve their performance in various applications. As you advance, consider experimenting with different architectures and hyperparameters to further enhance your models. Happy learning!