dennybritz/cnn-text-classification-tf
Cnn Text Classification Tf
This project is a TensorFlow implementation of a convolutional neural network designed for text classification. It functions as a deep learning text categorizer that assigns predefined labels to text documents by identifying and analyzing learned patterns within training sets.
The model utilizes a sequence of embedding-layer vectorization, convolutional layers for feature extraction, and max-pooling downsampling to process text data. Final category probabilities are determined through a dense-layer classification system.
The workflow covers the end-to-end machine learning lifecycle, including supervised text learning, model training with configurable embedding dimensions and filter sizes, and performance evaluation using validation datasets and checkpoint-based model persistence.
Features
- Text Classification - Assigns predefined categories to text documents using a convolutional neural network implemented in TensorFlow.
- Convolutional Neural Networks - Utilizes a convolutional neural network architecture to extract local features from text sequences.
- Text Classification Training - Provides a workflow for training deep learning models with configurable embedding dimensions and filter sizes for text.
- Deep Learning Classifiers - Uses a deep learning classifier to automate the categorization of text based on learned features.
- Pooling Layers - Applies max-pooling layers to downsample feature maps and reduce the dimensionality of text representations.
- Fully Connected Layers - Employs fully connected dense layers to map extracted features to final category probabilities.
- Convolutional Classifiers - Employs a convolutional classifier to extract local spatial patterns for text categorization.
- Supervised Text Learning - Implements a supervised learning system to automatically label and organize text documents based on training sets.
- Text Classification Models - Provides a convolutional neural network implementation using the TensorFlow ecosystem to classify text.
- Text Classifier Training - Trains deep-learning models to sort text into categories using a configurable convolutional architecture.
- Token Embedding Layers - Uses trainable embedding layers to transform discrete text tokens into dense vector representations.
- Model Performance Evaluators - Quantifies model reliability by comparing predicted categories against ground truth labels in a validation set.
- TensorFlow Graph Execution - Defines and executes the model's mathematical operations using TensorFlow's computational graph system.
- Model Accuracy Evaluators - Measures the classification accuracy of the trained model using a dedicated validation dataset.
- Model Implementations - Convolutional neural network for text classification tasks.
