mxnet3.R

#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
                    ,learning.rate = 0.03 #same as step size
                    ,eval.metric= mx.metric.accuracy
                    ,eval.data = list(data = test.x, label = test.y))