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1f4e4ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | 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()
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