Comprehensive Reference of Artificial Neural Network Tool Functions

When constructing artificial neural networks, various core tool functions are typically employed, primarily including activation functions, loss functions, and optimizers. Understanding the characteristics and appropriate application scenarios of these functions facilitates more efficient model design and training process fine-tuning.

Activation Functions Activation functions determine neuron output signals, with common implementations including: Sigmoid: Suitable for binary classification problems, outputs range between 0 and 1, but can suffer from gradient vanishing. In code implementation, typically applied as torch.sigmoid() or tf.nn.sigmoid(). ReLU (Rectified Linear Unit): Computationally simple and alleviates gradient vanishing issues, ideal for deep networks. Implemented as np.maximum(0, x) or framework-specific equivalents like torch.relu(). Tanh: Output range between -1 and 1, more suitable for certain scenarios than Sigmoid but still prone to gradient vanishing. Code implementation uses hyperbolic tangent functions from math libraries. Softmax: Standard choice for multi-class classification, transforms outputs into probability distributions. In practice, applied using torch.softmax() or tf.nn.softmax() with appropriate dimension specification.

Loss Functions Used to measure error between predicted and true values, common types include: Mean Squared Error (MSE): Suitable for regression problems, calculates squared difference between predictions and true values. Implemented as torch.nn.MSELoss() or manual calculation using mean((y_pred - y_true)**2). Cross-Entropy Loss: Preferred for classification tasks, especially when combined with Softmax outputs. Code implementation uses torch.nn.CrossEntropyLoss() or categorical_crossentropy in Keras. Huber Loss: Robust against outliers, combines characteristics of MSE and absolute error. Implementation requires conditional logic to switch between quadratic and linear loss based on error threshold.

Optimizers Optimizers adjust neural network weights, common implementations include: Stochastic Gradient Descent (SGD): Basic optimization method but converges slowly. Code implementation typically uses torch.optim.SGD() with learning rate and momentum parameters. Adam: Adaptive learning rate optimizer combining momentum and adaptive adjustments, widely used in deep learning. Implemented as torch.optim.Adam() with configurable beta parameters. RMSprop: Suitable for non-stationary objective functions, adjusts learning rate to enhance training stability. Implementation available as torch.optim.RMSprop() with decay rate parameter.

Additional Tool Functions Batch Normalization: Accelerates training and improves model stability. Code implementation uses torch.nn.BatchNorm1d/2d() or tf.keras.layers.BatchNormalization() between layers. Dropout: Prevents overfitting by randomly disabling neurons during training. Implemented as torch.nn.Dropout() with specified probability parameter. Learning Rate Scheduler: Dynamically adjusts learning rate to optimize training process. Common implementations include StepLR, ReduceLROnPlateau in PyTorch or callbacks in TensorFlow.

Mastering the characteristics of these tool functions enables more flexible construction and optimization of neural network models.