Build and train your own Neural Network

🚧 Under Development

This Neural Network Builder is currently under active development. Features and functionality may change. Use for educational and experimental purposes.

Neural Network Architecture

2 → 64 → 32 → 3

Input Layer

Hidden Layers

Output Layer

Loss Function

Current Architecture

Input Features:2

Hidden Layers:2 layers

Layer 1:64 neurons

Layer 2:32 neurons

Output Classes:3

Total Layers:4

Flow: Data flows from Input → Dense Layers → Activation → Prediction. Loss is calculated and backpropagated to update weights during training.

Training

Training Progress

Training graph will appear here

Click "Start Training" to begin

Testing & Prediction

Test Input Values

Feature 1

Feature 2

Prediction Result

Train a model first

Network Configuration

Choose a configuration preset

Hidden Layers Activation

ReLU (Rectified Linear Unit)

Formula: f(x) = max(0, x)

Returns x if positive, 0 otherwise. Most popular activation for hidden layers.

Pros:

• Fast computation
• No vanishing gradient

Cons:

• Dying ReLU problem
• Not zero-centered

Output Layer Activation

Softmax: Converts logits to probability distribution. Used for multi-class classification.

Build and train your own Neural Network

Neural Network Architecture

Current Architecture

Training

Training Progress

Testing & Prediction

Beginner

Standard (Recommended)

Deep Learning

Fast Training