# Save the modelmodel.save('path_to_my_model.h5')# Recreate the exact same model purely from the filenew_model = keras.models.load_model('path_to_my_model.h5')

**Here is what the above code is Doing:**

1. Save the weights of the model using the save_weights method.

2. Save the model architecture to a JSON file using the save_model method.

3. Save the entire model to a HDF5 file using the save method.

4. Recreate the exact same model, including its weights and the optimizer, from the HDF5 file.

Note that this technique saves everything: the architecture, weights, and optimizer state.

If you want to save only the architecture of a model, and not its weights or its training configuration, you can do so by serializing the model to JSON: