``rate_decay``
--------------

- Available in: Deep Learning
- Hyperparameter: yes

Description
~~~~~~~~~~~

The learning rate decay parameter controls the change of learning rate across layers. For example, assume the rate parameter is set to 0.01, and the rate_decay parameter is set to 0.5. Then the learning rate for the weights connecting the input and first hidden layer will be 0.01, the learning rate for the weights connecting the first and the second hidden layer will be 0.005, and the learning rate for the weights connecting the second and third hidden layer will be 0.0025, etc.

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

Related Parameters
~~~~~~~~~~~~~~~~~~

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

Example
~~~~~~~

.. example-code::
   .. code-block:: 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-block:: 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 perrformance
     model.model_performance(valid=True)