In PyBrain, you can save and load trained neural network models using the built-in
NetworkWriter and
NetworkReader classes, respectively. These classes allow you to serialize trained network objects to disk and deserialize them back into memory. Here's how you can save and load trained neural network models in PyBrain:
Save Trained Model : Use the
NetworkWriter class to save the trained network to a file.
from pybrain.tools.customxml import NetworkWriter
# Save the trained network to a file
NetworkWriter.writeToFile(trained_network, 'trained_network.xml')?
Load Trained Model : Use the
NetworkReader class to load the trained network from the saved file.
from pybrain.tools.customxml import NetworkReader
# Load the trained network from the saved file
trained_network = NetworkReader.readFrom('trained_network.xml')?
By following these steps, you can save trained neural network models to disk and load them back into memory in PyBrain. This is useful for persisting trained models and reusing them later for prediction or further training without the need to retrain from scratch. Additionally, you can serialize other PyBrain objects, such as datasets or trainers, using similar methods provided by the
NetworkWriter and
NetworkReader classes.
