Text Classification Using RNN on IMDB Dataset

This text classification tutorial demonstrates the implementation of a Recurrent Neural Network (RNN) on the IMDB large movie review dataset for sentiment analysis. The dataset comprises movie reviews labeled as either positive or negative sentiment.

Purpose

The code showcases:

• Setup and initialization using TensorFlow and TensorFlow Datasets (TFDS).
• Preprocessing of the IMDB dataset for binary sentiment classification.
• Building an RNN-based model using TensorFlow/Keras for sentiment analysis.
• Model training, evaluation, and visualization of training metrics.

Setup

This code requires TensorFlow and TensorFlow Datasets. Use the provided setup to install the necessary packages.

Input Pipeline

The dataset is split into training and test sets and processed using TensorFlow Datasets. The code demonstrates:

• Dataset loading with tfds.load.
• Shuffle and batch setup for training and test datasets.
• Visualization of text and label pairs.

Text Encoding

The raw text from the dataset is preprocessed using the TextVectorization layer. This layer adapts to the text and encodes it into indices for model input. The process involves setting vocabulary size, encoding text to indices, and reversing the encoding.

Model Architecture

The model architecture consists of the following layers:

• TextVectorization layer for encoding text.
• Embedding layer for word representation.
• Bidirectional LSTM layer for sequence processing.
• Dense layers for final classification.

Training and Evaluation

The code compiles and trains the model using a binary cross-entropy loss function and Adam optimizer. It tracks training accuracy, loss, and evaluates model performance on the test set.

Additional Techniques

Demonstrates the use of stacking multiple LSTM layers in the model architecture for improved performance. It visualizes training metrics using Matplotlib.

Usage

1. Setup: Install required packages.
2. Execution: Run code blocks sequentially to observe the training process and model evaluation.
3. Model Customization: Explore changing the model architecture or hyperparameters for different results.
4. Visualizations: Analyze training and validation metric plots to understand model performance.

Sample Predictions

The code includes examples of predicting sentiment for custom input sentences using the trained model.