`rate_annealing`¶

Available in: Deep Learning
Hyperparameter: yes

Description¶

Learning rate annealing reduces the learning rate to “freeze” into local minima in the optimization landscape. The annealing rate is the inverse of the number of training samples it takes to cut the learning rate in half. (For example, 1e-6 means that it takes 1e6 training samples to halve the learning rate.)

This parameter is only active when adaptive learning rate is disabled.

Example¶

r
python

library(h2o)
h2o.init()

# import the mnist datasets from the bigdata folder
train <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/train.csv.gz")
test <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/bigdata/laptop/mnist/test.csv.gz")

 # Turn response into a factor (we want classification)
 train[,785] <- as.factor(train[,785])
 test[,785] <- as.factor(test[,785])
 train <- h2o.assign(train, "train")
 test <- h2o.assign(test, "test")

 # Train a deep learning model
 dl_model <- h2o.deeplearning(x=c(1:784), y=785,
                              training_frame=train,
                              activation="RectifierWithDropout",
                              adaptive_rate=F,
                              rate=0.01,
                              rate_decay=0.9,
                              rate_annealing=1e-6,
                              momentum_start=0.95,
                              momentum_ramp=1e5,
                              momentum_stable=0.99,
                              nesterov_accelerated_gradient=F,
                              input_dropout_ratio=0.2,
                              train_samples_per_iteration=20000,
                              classification_stop=-1,  # Turn off early stopping
                              l1=1e-5
                             )

 # See the model performance
 print(h2o.performance(dl_model, test))