Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are well-suited for sequence prediction problems. This tutorial will guide you through the implementation of an RNN from scratch.

Introduction

RNNs are particularly useful for tasks such as language modeling, speech recognition, and time series analysis. They can process input sequences one element at a time and maintain a hidden state that encodes information about the sequence processed so far.

Installation

First, ensure you have the necessary libraries installed:

pip install numpy tensorflow

The RNN Model

The basic structure of an RNN consists of an input layer, hidden layer, and output layer.

Input Layer

The input layer receives the input sequence, which is typically represented as a matrix.

Hidden Layer

The hidden layer processes the input sequence using the RNN's recurrent mechanism. It maintains a hidden state that is updated as the sequence is processed.

Output Layer

The output layer produces the final prediction based on the hidden state.

Code Example

Here's a simple example of an RNN implemented in TensorFlow:

import tensorflow as tf

# Define the RNN model
def rnn_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.LSTM(128, input_shape=(None, 1)))
    model.add(tf.keras.layers.Dense(1))
    return model

# Create and compile the model
model = rnn_model()
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
# ...

Further Reading

For more detailed information on RNNs, you can read the following tutorials:

Image

Here's a visual representation of an RNN:

RNN Structure