Customizable Neural Network Training and Prediction (Animations + Formulas + Loss/Activation Functions)
Customizable Neural Network Training and Prediction
This example demonstrates a multi-layer neural network where you can customize the network structure, activation functions, and loss functions as needed.
Both training and prediction are shown with animations illustrating forward propagation (with back propagation update during training), accompanied by formulas and statistical information.
In-depth Understanding of Neural Network Basic Elements and Calculation Methods
In this example, we introduce the core components of a neural network—parameters and hyperparameters—and explain how they function in model computation and practical applications.
Parameters
Parameters refer mainly to the weights and biases within the network. During forward propagation, input data is computed as:
a = activation(Σ(x * weight) + bias)
where x is the input, weight is the connection strength, and bias is the offset. The activation function introduces non-linearity. During training, these weights and biases are updated via back propagation based on the gradient of the loss function.
Hyperparameters
Hyperparameters are preset configurations defined by the user before training and are not adjusted automatically during the training process. Common hyperparameters include:
- Learning Rate: Controls the step size during parameter updates, affecting convergence speed and stability.
- Epoch Count: The number of times the entire dataset is passed through the network, determining the intensity and duration of training.
- Network Structure: The number of neurons in the input, hidden, and output layers which directly influence the model’s capacity to represent the data.
- Activation and Loss Functions: They respectively dictate how each layer processes information non-linearly and how the error is calculated.
Proper configuration of hyperparameters can help prevent overfitting or underfitting, ensuring that the network operates efficiently and stably. For example, a learning rate that is too high might cause the parameters to overshoot their optimal values, while a learning rate that is too low leads to slow training.
Underlying Principles and Practical Applications
The operation of a neural network mainly comprises two stages: forward propagation and back propagation:
- Forward Propagation: Input data moves from the input layer through hidden layers to the output layer. Each neuron computes its output using the formula above. This step determines the prediction results.
- Back Propagation: The error between the predicted and target outputs is computed, and gradients are calculated based on the derivative of the activation functions. The weights and biases are then updated according to the learning rate to reduce the error.
In practice, this mechanism is used for a variety of applications such as image classification, speech recognition, and question-answer systems. The animations and formulas in the demo help users intuitively understand how each element interacts and observe the detailed changes during the training and prediction processes.