Character classification - rnn using LSTM

[vijayzutshi]

HI,

I am using LSTM to build a character classification model. I have also taken idea from your code too. I am able to build a model and infer model using following codes:-

model <- mx.lstm(X.train, X.val,
ctx=mx.cpu(),
num.round=num.round,
update.period=update.period,
num.lstm.layer=num.lstm.layer,
seq.len=seq.len,
num.hidden=num.hidden,
num.embed=num.embed,
num.label=vocab,
batch.size=batch.size,
input.size=vocab,
initializer=mx.init.uniform(0.01),
learning.rate=learning.rate,
wd=wd,
optimizer = "sgd",
clip_gradient=clip_gradient)

infer.model <- mx.lstm.inference(num.lstm.layer=num.lstm.layer,
input.size=vocab,
num.hidden=num.hidden,
num.embed=num.embed,
num.label=vocab,
arg.params=model$arg.params,
ctx=mx.cpu())

Now what I want to do is to find the accuracy of the model using R with ROC curve. I would really appreciate if you or someone can provide me with a code in R to do that. Your help will be really appreciated as I not able to figure out as to the procedure or code.

Thanks
Vijay Zutshi