Neural Network Training with Real-Time Plotting

This script demonstrates a simple neural network training process using gradient descent. The network processes a set of training data, adjusts its weights and biases based on the error, and visualizes the outputs in real-time.

Features

• Neural Network Training: Utilizes a basic form of a neural network to process training data.
• Gradient Descent: Employs gradient descent to optimize the network's weights and biases.
• Real-Time Plotting: Visualizes the network's output for each training iteration in real-time using Matplotlib.

Code Description

The script is structured as follows:

1. Softplus Activation Function: Defines a softplus function used as the activation function in the neural network.

2. Derivative Functions: Includes derivative functions (derW1, derW2, etc.) to compute gradients for gradient descent.

3. Initial Setup: Sets up the training data and initializes the weights and biases.

4. Plotting Setup: Configures a Matplotlib plot for real-time visualization.

5. Training Loop:

   • The main loop runs for a predetermined number of iterations.
   • In each iteration, the network processes the training data.
   • The derivatives of the weights and biases are calculated.
   • The weights and biases are updated using the calculated derivatives.
   • The outputs are visualized in real-time on the plot.

6. Finalization: Turns off the interactive mode and displays the final plot.

Usage

• Ensure numpy and matplotlib are installed in your Python environment.
• Adjust the learning_rate and the number of iterations as per your requirements.
• Run the script in an environment that supports real-time plotting (like VS Code).

Note

Real-time plotting can slow down the training process, especially for a large number of iterations. Adjust the frequency of plot updates for optimal performance.