Bear in mind that, this tutorial only uses the first 10,000 images with some arbitrary value for initial_learning_rate=0.01, validation_split=0.2 and batch_size=64. Looks like Constant and Time-based learning rates have better performance than Step decay and Exponential decay for this particular tutorial. initial_learning_rate = 0.01 def lr_step_decay(epoch, lr): drop_rate = 0.5 epochs_drop = 10.0 return initial_learning_rate * math.pow(drop_rate, math.floor(epoch/epochs_drop)) # Fit the model to the training data history_step_decay = model.fit( X_train, y_train, epochs=100, validation_split=0.2, batch_size=64, callbacks=, )Īnd below are the plots of the accuracy and learning rate. Similarly, we can implement this by defining a step decay function lr_step_decay() and pass it to LearningRateScheduler callback. Where initial_lr is the initial learning rate such as 0.01, the drop_rate is the amount that the learning rate is modified each time if it is changed, epoch is the current epoch number, and epochs_drop is how often to change the learning rate such as 10 epochs. Formally, it is defined as: learning_rate = initial_lr * drop_rate^floor(epoch / epochs_drop) Step decayĪnother popular learning rate schedule is to systematically drop the learning rate at specific times during training. initial_learning_rate = 0.01 epochs = 100 decay = initial_learning_rate / epochs def lr_time_based_decay(epoch, lr): return lr * 1 / (1 + decay * epoch) history_time_based_decay = model.fit( X_train, y_train, epochs=100, validation_split=0.2, batch_size=64, callbacks=, )Īnd below are the plots of accuracy and learning rate. In Keras, one way to implement the time-based decay is by defining a time-based decay function lr_time_based_decay() and pass it to LearningRateScheduler callback. The value of decay is normally implemented as decay = initial_learning_rate / num_of_epoches When the decay is specified, it will decrease the learning rate from the previous epoch by the given fixed amount. When the decay is zero, this has no effect on changing the learning rate. Where lr is the previous learning rate, decay is a hyperparameter and epoch is the iteration number. Formally, the time-based decay is defined as: learning_rate = lr * 1 / (1 + decay * epoch) Time-based decay is one of the most popular learning rate schedules.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |