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| import tensorflow as tf | |
| import numpy as np | |
| from utils.model_utils import create_model, convert_to_tflite | |
| def create_sample_model(): | |
| """Create a sample model for demonstration purposes.""" | |
| print("Creating sample sign language model...") | |
| # Model parameters | |
| num_classes = 10 # Number of signs in labels.txt | |
| sequence_length = 30 | |
| num_features = 126 # 63 landmarks + 63 velocity features | |
| # Create model | |
| model = create_model(num_classes, sequence_length, num_features) | |
| # Generate dummy training data | |
| X_train = np.random.random((1000, sequence_length, num_features)).astype(np.float32) | |
| y_train = np.random.randint(0, num_classes, (1000,)) | |
| # Generate dummy validation data | |
| X_val = np.random.random((200, sequence_length, num_features)).astype(np.float32) | |
| y_val = np.random.randint(0, num_classes, (200,)) | |
| print("Training sample model...") | |
| # Train for a few epochs | |
| history = model.fit( | |
| X_train, y_train, | |
| validation_data=(X_val, y_val), | |
| epochs=5, | |
| batch_size=32, | |
| verbose=1 | |
| ) | |
| # Save the model | |
| model.save('models/sign_language_model.h5') | |
| print("Model saved as 'models/sign_language_model.h5'") | |
| # Convert to TFLite | |
| print("Converting to TensorFlow Lite...") | |
| converter = tf.lite.TFLiteConverter.from_keras_model(model) | |
| converter.optimizations = [tf.lite.Optimize.DEFAULT] | |
| tflite_model = converter.convert() | |
| with open('models/model.tflite', 'wb') as f: | |
| f.write(tflite_model) | |
| print("TFLite model saved as 'models/model.tflite'") | |
| print("Sample model creation completed!") | |
| if __name__ == "__main__": | |
| create_sample_model() | |