Keras RNN Tutorial

This tutorial provides a step-by-step guide on how to build and train a Recurrent Neural Network (RNN) using Keras. RNNs are powerful models used for sequential data processing, making them suitable for tasks like time series analysis, natural language processing, and more.

What is Keras?

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It makes building and training neural networks as simple as possible.

Recurrent Neural Networks (RNNs)

RNNs are designed to work with sequences of data. Unlike traditional neural networks, RNNs have loops allowing information to persist between steps.

Tutorial Overview

  • Setup - Install necessary libraries
  • Data Preparation - Load and preprocess the dataset
  • Model Building - Construct the RNN model
  • Training - Train the model with your data
  • Evaluation - Test the model's performance

Setup

First, make sure you have the required libraries installed:

pip install numpy tensorflow keras

Data Preparation

In this tutorial, we'll use the famous IMDB dataset, which contains 50,000 movie reviews. Each review is a sequence of words.

from keras.datasets import imdb
from keras.preprocessing import sequence


(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)

# Pad sequences
max_len = 500
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)

Model Building

Let's build a simple RNN model with one LSTM layer.

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(50, input_shape=(max_len, 1)))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Training

Train the model with the training data.

model.fit(x_train, y_train, epochs=10, batch_size=128)

Evaluation

Evaluate the model's performance on the test set.

loss, accuracy = model.evaluate(x_test, y_test)
print(f'Accuracy: {accuracy * 100:.2f}%')

For more information on Keras RNNs, check out our Keras RNN Deep Dive.