1. Multiclass MLP BP Learning with the Iris Dataset - Python Example

• Output Layer Activation Function: Softmax is used. Softmax is applied in the output layer to calculate probabilities for each class in a multi-class classification problem.

• Loss Function: Cross-entropy loss is applied, as it is commonly used alongside Softmax to measure the difference between the predicted probability distribution and the actual class labels.

• Backpropagation: Backpropagation is performed by computing the derivative of the cross-entropy loss for the Softmax output layer. This approach is the most common for multi-class classification tasks.

• Code example: multiclass_mlp_bp.py

2. MLP Learning with Backpropagation Using the Iris Dataset - Python Example

• Output Layer Activation Function: Sigmoid or a simple linear activation function can be used. Sigmoid is generally used for binary or multi-label binary classification tasks, as it outputs probability values, though they do not sum to 1 across classes.

• Loss Function: Mean Squared Error (MSE) loss is applied, which is often used for regression tasks or when Sigmoid is used in the output layer, though it is less suited for multi-class classification than cross-entropy loss.

• Backpropagation: Backpropagation is carried out using the Sigmoid activation function and the MSE loss function. This approach is more commonly used in binary classification or regression tasks.

• Code example: mlp_bp.py

Summary of Key Differences:

1. Output Layer Activation Function:
• Softmax (Multiclass MLP): Calculates the probability for each class in multi-class classification.
• Sigmoid (MLP Learning): Used for binary or multi-label classification but is less suitable for multi-class classification.

2. Loss Function:
• Cross-Entropy Loss (Multiclass MLP): More appropriate for classification tasks with probability-based output.
• MSE (MLP Learning): Used in regression or simple classification but is less suitable for classification tasks.

3. Backpropagation Method:
• In multi-class classification, backpropagation using Softmax and cross-entropy loss is more common, enabling accurate learning of class probabilities.
• When using MSE and Sigmoid, output values are not easily interpretable as probabilities, which can lead to lower accuracy for multi-class classification tasks.

Conclusion:

• The Multiclass MLP example is tailored for multi-class classification, using Softmax and cross-entropy loss to learn probabilities for each class.

• The MLP example is a more general method using specific activation and loss functions, which may be less suitable for multi-class classification tasks.