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deepmodel <- h2o.deeplearning(x = x	 	 
                             ,y = y	 	 
                             ,training_frame = trainh2o	 	 
                             ,validation_frame = testh2o	 	 
                             ,standardize = T	 	 
                             ,model_id = "deep_model"	 	 
                             ,activation = "Rectifier"	 	 
                             ,epochs = 100	 	 
                             ,seed = 1	 	 
                             ,hidden = 5	 	 

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path = "~/mydata/deeplearning"
setwd(path)

#load libraries
library(data.table)
library(mlr)
#set variable names
setcol <- c("age",
           "workclass",
           "fnlwgt",

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#load the package
require(h2o)

#start h2o
localH2o <- h2o.init(nthreads = -1, max_mem_size = "20G")

#load data on H2o
trainh2o <- as.h2o(train)
testh2o <- as.h2o(test)

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#set parameter space
activation_opt <- c("Rectifier","RectifierWithDropout", "Maxout","MaxoutWithDropout")
hidden_opt <- list(c(10,10),c(20,15),c(50,50,50))
l1_opt <- c(0,1e-3,1e-5)
l2_opt <- c(0,1e-3,1e-5)

hyper_params <- list( activation=activation_opt,
                     hidden=hidden_opt,
                     l1=l1_opt,
                     l2=l2_opt )

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# # Installation - Windows
install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("mxnet")
library(mxnet)

#Installation - Linux
#Press Ctrl + Alt + T and run the following command
sudo apt-get update
sudo apt-get -y install git

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#load package
require(mxnet)

#convert target variables into numeric
train[,target := as.numeric(target)-1]
test[,target := as.numeric(target)-1]

#convert train data to matrix
train.x <- data.matrix(train[,-c("target"),with=F])
train.y <- train$target

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#set seed to reproduce results
mx.set.seed(1)

mlpmodel <- mx.mlp(data = train.x
                    ,label = train.y
                    ,hidden_node = 3 #one layer with 10 nodes
                    ,out_node = 2
                    ,out_activation = "softmax" #softmax return probability
                    ,num.round = 100 #number of iterations over training data
                    ,array.batch.size = 20 #after every batch weights will get updated

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#create NN structure
data <- mx.symbol.Variable("data")
fc1 <- mx.symbol.FullyConnected(data, num_hidden=3) #3 neuron in one layer
lrm <- mx.symbol.SoftmaxOutput(fc1)

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nnmodel <- mx.model.FeedForward.create(symbol = lrm
                                        ,X = train.x
                                        ,y = train.y
                                        ,ctx = mx.cpu()
                                        ,num.round = 100
                                        ,eval.metric = mx.metric.accuracy
                                        ,array.batch.size = 50
                                        ,learning.rate = 0.01)

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#configure another network
data <- mx.symbol.Variable("data")
fc1 <- mx.symbol.FullyConnected(data, name = "fc1", num_hidden=10) #1st hidden layer
act1 <- mx.symbol.Activation(fc1, name = "sig", act_type="relu") 
fc2 <- mx.symbol.FullyConnected(act1, name = "fc2", num_hidden=2) #2nd hidden layer
out <- mx.symbol.SoftmaxOutput(fc2, name = "soft")