Model Type: Neural Network
Framework: TensorFlow, Keras
Purpose: To approximate the cosine function using a neural network.
This model is designed for educational purposes to demonstrate how a neural network can learn and approximate a mathematical function, specifically the cosine function. It can be used as a tutorial or a starting point for similar function approximation tasks.
The model is a fully connected feedforward neural network (Multi-Layer Perceptron) trained to approximate the cosine function. The network takes a single input (a value in radians) and produces a single output (the cosine of the input value).
The training data consists of pairs of input values (in radians) and their corresponding cosine values. The data is generated using the numpy library and split into training and testing sets.
- Data Generation: Generate input values uniformly distributed over a specified range and compute their cosine values.
- Model Definition: Define a neural network with an appropriate number of layers and neurons.
- Compilation: Compile the model with a loss function (e.g., Mean Squared Error) and an optimizer (e.g., Adam).
- Training: Train the model on the generated data with appropriate callbacks for early stopping and model checkpointing.
The model's performance is evaluated using Mean Squared Error (MSE) on a separate test set.
Performance is visualized by plotting the predicted cosine values against the true cosine values. The plots are saved to a directory called saved_plots.
- The model is trained on a specific range of input values. Its performance outside this range may not be reliable.
- This is a simple model intended for educational purposes and may not be optimized for high-precision applications.