shaadclt / 3D-Surface-Prediction-Tensorflow

This project demonstrates the creation of a 3D surface prediction model using TensorFlow. The model is trained on a randomly generated dataset and visualizes the predicted surface in a 3D space.

3D Surface Prediction using TensorFlow

This project demonstrates the creation of a 3D surface prediction model using TensorFlow. The model is trained on a randomly generated dataset and visualizes the predicted surface in a 3D space.

Getting Started

Prerequisites

• TensorFlow
• NumPy
• Matplotlib

Installation

pip install tensorflow numpy matplotlib

Usage

1. Open the Jupyter Notebook file ANN_Regression.ipynb.
2. Run the notebook cell by cell to execute the code.
3. Visualize the generated dataset and the predicted 3D surface.

Dataset Generation

The dataset is generated with 1000 data points, uniformly distributed between (-3, +3). The target variable Y is calculated as the cosine of (2X[:,0]) plus the cosine of (3X[:,1]).

Model Architecture

The neural network model consists of:

• Input layer: Dense layer with 128 neurons and ReLU activation.
• Output layer: Dense layer with 1 neuron.

Model Training

The model is trained using Mean Squared Error (MSE) loss and the Adam optimizer.

Visualization

Visualize the loss during training and plot the predicted 3D surface.