# an LeNet mxnet 
library(mxnet)

#read the data in
train = data.matrix(read.csv("../input/train.csv", header=T))
test = data.matrix(read.csv("../input/test.csv", header=T))

train.x = train[,-1]
train.y = train[,1]

#normalize the data 
train.x =  t(train.x/255)
test = t(test/255)

#Build LeNet architecture

# input
data = mx.symbol.Variable('data')
# first conv
conv1 = mx.symbol.Convolution(data=data, kernel=c(5,5), num_filter=20)
tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh")
pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max",
                          kernel=c(2,2), stride=c(2,2))
# second conv
conv2 = mx.symbol.Convolution(data=pool1, kernel=c(5,5), num_filter=50)
tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh")
pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max",
                          kernel=c(2,2), stride=c(2,2))
# first fullc
flatten = mx.symbol.Flatten(data=pool2)
fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)
tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh")
# second fullc
fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=10)
# output class probabilities
lenet = mx.symbol.SoftmaxOutput(data=fc2)

train.array = train.x
dim(train.array)= c(28, 28, 1, ncol(train.x))
test.array = test
dim(test.array) = c(28, 28, 1, ncol(test))

device.cpu = mx.cpu()

mx.set.seed(0)
model = mx.model.FeedForward.create(lenet, X=train.array, y=train.y,
                                     ctx=device.cpu, num.round=6, array.batch.size=100,
                                     learning.rate=0.05, momentum=0.9, wd=0.00001,
                                     eval.metric=mx.metric.accuracy,
                                     epoch.end.callback=mx.callback.log.train.metric(100))
preds = predict(model, test.array)
pred.label = max.col(t(preds)) - 1
submission = data.frame(ImageId=1:ncol(test), Label=pred.label)
write.csv(submission, file='submission.csv', row.names=FALSE, quote=FALSE)