CONFUSION MATRIX, R, - r

Question

I need little help with the following code below. I have to setup a loop to train a neural network model on the TRAINING data with a different number of epochs each time by starting from 5 and adding 3 until I reach 20. Then I have to calculate a line chart showing the accuracy with differing numbers of epochs. I also have to keep all the parameters same as shown. Much of the code is what was given by our instructor. I added the epochs= c(5,8,11,14,17,20) to create a list of epochs and the error.rate = vector() where I intend to store the accuracy from each loop into a vector. The accuracy I want is from the confusion matrix and is found from the formula
h2o.hit_ratio_table(<model>,train = TRUE)[1,2]
The problem I face is that I have tried to create a loop. I am trying to get the results from each loop. I have labled the first part of the loop as X to try to put it into the vector for the accuracy for each loop into a vector error.rate=h2o.hit_ratio_table(x,train=TRUE)[1,2]).
However, it gives an error.
> Error in is(object, "H2OModelMetrics") : object 'X' not found In
> addition: Warning messages: 1: In 1:epochs : numerical expression has
> 6 elements: only the first used
Moreover, when I remove the error.rate=...... part, the function runs fine but there is no way to find the values of the accuracy.
I am a noob at R so a little help will be much appreciated.
s <- proc.time()
epochs= c(5,8,11,14,17,20)
error.rate = vector()
for (epoch in 1:epochs){#set up loop to go around 6 times
X=h2o.deeplearning(x = 2:785, # column numbers for predictors
y = 1, # column number for label
training_frame = train_h2o, # data in H2O format
activation = "RectifierWithDropout", # mathematical activation function
input_dropout_ratio = 0.2, # % of inputs dropout, because some inputs might not matter.
hidden_dropout_ratios = c(0.25,0.25,0.25,0.25), # % for nodes dropout, because maybe we don't need full connections. Improves generalisability
balance_classes = T, # over/under samples so that all classes are similar size.
hidden = c(50,50,50,50), # two layers of 100 nodes
momentum_stable = 0.99,
nesterov_accelerated_gradient = T,
error.rate=h2o.hit_ratio_table(x,train=TRUE)[1,2])
proc.time() - s}

Answer 1

You are doing for(epoch in 1:epochs). Here the 'epoch' term changes each loop (and usually you use it within the loop but i don't see it?). 1:epochs will not work as you think it should. It is taking the first element of epochs (5) and basically saying for(epoch in 1:5) where epoch is 1, then 2, ... and then 5. You want something like for(epoch in epochs) and if you DO want a sequence from 1:each epoch in your code you should write it within the loop.
Also, x is rewritten each time it loops. You should initialize it and save subsets of it each loop instead:
epochs= c(5,8,11,14,17,20)
x <- list() # save as list #option 1
y <- list() # for an option 2
for (epoch in epochs){ #set up loop to go around 6 times
X[[epoch]] = h2o.deeplearning(... )
# or NOW you can somehow use 1:epoch where each loop epoch changes
}
But I would really focus on there is no use of using your epoch in your for loop as I see in your post. Perhaps find out where you want to use it...