RNN Neural Network Simulator

Interactive visualization of Recurrent Neural Networks. Understand how RNNs process sequential data through time.

Network Configuration

RNN Type: Simple RNN LSTM (Long Short-Term Memory) GRU (Gated Recurrent Unit) Bidirectional RNN

Hidden Layer Size: 8

Time Steps: 5

Sequence Type: Binary Sequence (0/1) Text Sequence (A-Z) Numeric Sequence (1-9) Custom Sequence

Custom Sequence:

Learning Rate: 0.1

Training Epochs: 50

Network Visualization

Input Sequence

Click on sequence items to activate them. The RNN will process them one at a time through the time steps.

Network Output & States

Hidden States Over Time

Each square represents a neuron in the hidden layer. Color intensity shows activation level.

Current Output

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Predicted next value in sequence based on current hidden state

Training Progress

Loss: 0.000 Epoch: 0/50 Accuracy: 0%

About RNNs (Recurrent Neural Networks)

How RNNs Work

RNNs have a "memory" that captures information about what has been calculated so far. They process sequences one element at a time while maintaining a hidden state that contains information about previous elements.

• Process sequential data
• Maintain internal memory
• Share parameters across time steps
• Handle variable length sequences

RNN Applications

RNNs are used in many sequence-based tasks:

• Text Generation - Predict next word
• Speech Recognition - Audio to text
• Time Series Prediction - Stock prices, weather
• Machine Translation - Language translation
• Sentiment Analysis - Text classification

Forward Propagation

At each time step, the RNN:

1. Takes input and previous hidden state
2. Computes new hidden state
3. Produces output
4. Passes hidden state to next time step

Backpropagation Through Time

RNNs are trained using Backpropagation Through Time (BPTT):

• Unroll the network through time
• Calculate errors at each step
• Update weights recursively
• Handle vanishing/exploding gradients

RNN Neural Network Simulator | Educational Tool for Understanding Recurrent Neural Networks

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