``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.

Related Paramters
~~~~~~~~~~~~~~~~~

- `rate <rate.html>`__
- `rate_decay <rate_decay.html>`__

Example
~~~~~~~

.. tabs::
   .. code-tab:: r R

        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))

   .. code-tab:: python

         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)