gaussic/text-classification-cnn-rnn
Text Classification Cnn Rnn
Dieses Projekt ist ein TensorFlow-basierter, überwachter Textkategorisierer, der für chinesisches Natural Language Processing konzipiert ist. Er nutzt eine hybride neuronale Netzwerkarchitektur, die konvolutionale und rekurrente Schichten kombiniert, um rohen chinesischen Text auf vordefinierte Kategorien abzubilden.
Das System integriert konvolutionale neuronale Netze für die lokale Feature-Extraktion und rekurrente neuronale Netze zur Analyse sequenzieller Abhängigkeiten. Es verwendet zeichenbasierte Tokenisierung und Word-Embeddings, um Text als numerische Tensoren darzustellen.
Die Implementierung deckt den End-to-End-Machine-Learning-Pipeline ab, einschließlich Text-Preprocessing, iterativem Modelltraining mit Dropout-Regularisierung und Messung der Vorhersageleistung mittels Verlust- und Genauigkeitsmetriken. Es enthält zudem Hilfsprogramme zur Verfolgung des Trainingsfortschritts und zur Aufrechterhaltung des Modellzustands durch Checkpoints.
Features
- Chinese Text Classifiers - Provides a specialized machine learning tool for assigning categories to Chinese text documents.
- Convolutional Neural Networks - Uses convolutional neural networks to extract local features and n-gram patterns from text.
- Hybrid CNN-RNN Architectures - Employs a hybrid CNN-RNN architecture to analyze both local features and long-term sequential dependencies.
- Recurrent Neural Networks - Implements text classification using recurrent neural networks to capture long-term sequential dependencies.
- Model Training Pipelines - Implements a complete training pipeline to optimize neural network weights for predictive accuracy.
- Recurrent Neural Networks - Utilizes recurrent neural networks to capture sequential dependencies and temporal patterns in text.
- Supervised Text Categorizers - Implements a supervised training and inference pipeline for predicting text labels.
- Text Classification Models - Implements a TensorFlow-based model for categorizing Chinese text documents.
- Text Preprocessing Pipelines - Provides a pipeline to convert raw Chinese text into fixed-length numerical sequences.
- Word Embeddings - Maps Chinese characters to numerical vectors for representation within the neural network.
- Text Classification Training - Trains convolutional neural networks specifically for the task of text document classification.
- Dropout Regularization - Employs dropout regularization to prevent overfitting and improve model generalization.
- Iterative Training - Implements iterative training loops to optimize model weights over multiple epochs.
- Model Performance Evaluators - Quantifies classifier quality using loss and accuracy metrics across training and validation sets.
- Model Prediction Evaluation - Calculates loss and accuracy metrics by comparing model predictions against ground truth labels.
- Model Training - Follows iterative procedures to train predictive models on prepared text datasets.
- Training Weight Adjustments - Updates internal connection strengths using gradient descent to minimize prediction error.
- TensorFlow Graph Execution - Defines the mathematical operations and layer connectivity using TensorFlow's computational graphs.
- Convolutional Text Features - Extracts hierarchical spatial patterns and local features from text using CNNs.
- LLM Category Assignments - Assigns predefined categories to input text using a trained neural network model.
- Text Classification - Implementation examples for news classification using neural networks.
