Saving and loading models is an essential part of deep learning. After training a model, you often want to reuse it later without retraining. This helps save time, allows deployment in real-world applications, and makes it easier to share models with others.<\/p>\n\n\n\n
Why Save a Model?<\/strong><\/p>\n\n\n\n What Can Be Saved?<\/strong><\/p>\n\n\n\n Saving Models in Practice<\/strong><\/p>\n\n\n\n 1. Saving Entire Model (Recommended)<\/strong> Example (TensorFlow\/Keras)<\/strong><\/p>\n\n\n\n 2. Saving Only Weights<\/strong> Loading Models<\/strong><\/p>\n\n\n\n 1. Loading Entire Model<\/strong><\/p>\n\n\n\n 2. Loading Weights into a Model<\/strong><\/p>\n\n\n\n Saving and Loading in PyTorch<\/strong><\/p>\n\n\n\n Saving Model<\/strong><\/p>\n\n\n\n Loading Model<\/strong><\/p>\n\n\n\n Best Practices<\/strong><\/p>\n\n\n\n Applications<\/strong><\/p>\n\n\n\n Lesson Summary<\/strong>\n
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This method stores both the model structure and weights together.<\/p>\n\n\n\nfrom tensorflow import keras# Save model
model.save(\"model.h5\")<\/pre>\n\n\n\n
Useful when you want to reuse the same architecture but update weights separately.<\/p>\n\n\n\nmodel.save_weights(\"weights.h5\")<\/pre>\n\n\n\n
from tensorflow import kerasmodel = keras.models.load_model(\"model.h5\")<\/pre>\n\n\n\n
model.load_weights(\"weights.h5\")<\/pre>\n\n\n\n
import torchtorch.save(model.state_dict(), \"model.pth\")<\/pre>\n\n\n\n
model.load_state_dict(torch.load(\"model.pth\"))
model.eval()<\/pre>\n\n\n\n\n
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Saving and loading models allows you to preserve trained neural networks for future use. By storing model architecture and weights, you can quickly reload models, make predictions, and deploy them efficiently. This is a critical step in building practical and scalable AI solutions.<\/p>\n\n\n