Tutorial dan Simulasi Perhitungan Jaringan Syaraf Tiruan Model Backpropagation

Summary

This script demonstrates the process of forward propagation in a neural network. It explains how two inputs (X1 and X2) are multiplied by their respective weights (W1, W2, W3, W4, W5) and added to biases to calculate values for hidden layers and output. The sigmoid activation function is applied at each stage, including the final output layer. The result of the propagation is computed as approximately 0.80493, providing a practical example of how back-propagation and activation functions influence the output in a neural network.

Takeaways

• 😀 The script discusses a neural network example involving two inputs (X1 and X2), a hidden layer, and one output.
• 😀 The weights (W1, W2, W3, W4, etc.) are initially assigned small random values for the model.
• 😀 The first step is to compute the values for the hidden layer using the inputs and weights (W1, W2, W3, W4).
• 😀 The first calculation for the hidden layer is X1 multiplied by W1 plus X2 multiplied by W2, then adding a bias value of 1.5.
• 😀 The second calculation for the hidden layer involves X1 multiplied by W3 and X2 multiplied by W4, plus a bias of 1.6.
• 😀 The values of the hidden layer are computed using the sigmoid activation function, resulting in outputs of 0.75 and 0.83.
• 😀 The next step involves calculating the output layer by combining the hidden layer values and additional weights (W5, W6) along with a bias (B3).
• 😀 The output layer calculation yields a result of 1.41 before applying the sigmoid activation function again.
• 😀 After applying the sigmoid function to the output layer, the final result is approximately 0.80.
• 😀 The script illustrates the process of forward propagation in a neural network, demonstrating how input values pass through layers to produce an output.
• 😀 The final output of 0.80 shows how forward propagation can be used to calculate the output of a neural network after all computations and activations.

Q & A

What is the purpose of the forward propagation process described in the script?

The forward propagation process is used to calculate the output of a neural network by passing input through layers, applying weights and biases, and using activation functions to derive the final output.

What is the first step in calculating the hidden layer values in this neural network?

The first step is to calculate the values for the hidden layer by multiplying the inputs (X1 and X2) with their respective weights (W1, W2, W3, W4) and adding the biases (B1, B2).

How are the values for the hidden layer computed in this example?

The hidden layer values are computed by multiplying the inputs with their respective weights and adding the bias for each layer. For example, the first hidden layer is calculated as (X1 * W1) + (X2 * W2) + B1.

What activation function is used in this forward propagation example?

The activation function used is the sigmoid function, which is defined as σ(x) = 1 / (1 + e^(-x)). This function helps introduce non-linearity into the model.

What are the results of applying the sigmoid function to the hidden layer values?

After applying the sigmoid function to the hidden layer values, the results are 0.75 for the first hidden layer and 0.83 for the second hidden layer.

How is the output layer value calculated?

The output layer value is calculated by multiplying the hidden layer values (X1 and X2) with their respective weights (W5, W6) and adding the bias (B3). This result is then passed through the sigmoid activation function.

What is the final output after applying the sigmoid function to the output layer?

The final output after applying the sigmoid function to the output layer is 0.80493.

Why is the sigmoid activation function used in this process?

The sigmoid function is used because it outputs values between 0 and 1, which is useful for modeling probabilities and ensuring the outputs of the network are bounded.

What does the script illustrate about the process of backpropagation in a neural network?

While the script does not go into detail about backpropagation, it illustrates the forward propagation step, which is essential for calculating the network's output. Backpropagation uses the output to adjust weights in the network during training.

What role do the weights (W1, W2, etc.) and biases (B1, B2, etc.) play in the forward propagation process?

The weights and biases adjust the strength and influence of the inputs as they pass through the network, helping the model learn and make predictions. They are critical parameters that are tuned during training.