- Dynamically adjust the learning rate during training to improve model convergence.pythonCopy code
from tensorflow.keras.callbacks import LearningRateScheduler def scheduler(epoch, lr): if epoch < 10: return lr else: return lr * 0.99 lr_scheduler = LearningRateScheduler(scheduler) model.fit(X_train, y_train, epochs=50, callbacks=[lr_scheduler])
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