`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¶

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))

import h2o
h2o.init()
from h2o.estimators.deeplearning import H2ODeepLearningEstimator

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

# Set the predictors and response column.
# Turn response into a factor
predictors = list(range(0,784))
resp = 784
train[resp] = train[resp].asfactor()
test[resp] = test[resp].asfactor()
nclasses = train[resp].nlevels()[0]

# Train a deep learnring model
model = H2ODeepLearningEstimator(activation="RectifierWithDropout",
                                 adaptive_rate=False,
                                 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=False,
                                 input_dropout_ratio=0.2,
                                 train_samples_per_iteration=20000,
                                 classification_stop=-1,  # Turn off early stopping
                                 l1=1e-5
                                )
model.train (x=predictors,y=resp, training_frame=train, validation_frame=test)

# See the model performance
model.model_performance(valid=True)