MXNET softmax output: label shape confusion - r

Question

I have not got a clear idea about how labels for the softmax classifier should be shaped.
What I could understand from my experiments is that a scalar laber indicating the index of class probability output is one option, while another is a 2D label where the rows are class probabilities, or one-hot encoded variable, like c(1, 0, 0).
What puzzles me though is that:
I can use sclalar label values that go beyong indexing, like 4 in my
example below -- without warning or error. Why is that?
When my label is a negative scalar or an array with a negative value,
the model converges to uniform probablity distribution over classes.
For example, is this expected that actor_train.y = matrix(c(0, -1,v0), ncol = 1) results in equal probabilities in the softmax output?
I try to use softmax MXNET classifier to produce the policy gradient
reifnrocement learning, and my negative rewards lead to the issue
above: uniform probability. Is that expected?
require(mxnet)
actor_initializer <- mx.init.Xavier(rnd_type = "gaussian",
factor_type = "avg",
magnitude = 0.0001)
actor_nn_data <- mx.symbol.Variable('data') actor_nn_label <- mx.symbol.Variable('label')
device.cpu <- mx.cpu()
NN architecture
actor_fc3 <- mx.symbol.FullyConnected(
data = actor_nn_data
, num_hidden = 3 )
actor_output <- mx.symbol.SoftmaxOutput(
data = actor_fc3
, label = actor_nn_label
, name = 'actor' )
crossentfunc <- function(label, pred)
{
- sum(label * log(pred)) }
actor_loss <- mx.metric.custom(
feval = crossentfunc
, name = "log-loss"
)
initialize NN
actor_train.x <- matrix(rnorm(11), nrow = 1)
actor_train.y = 0 #1 #2 #3 #-3 # matrix(c(0, 0, -1), ncol = 1)
rm(actor_model)
actor_model <- mx.model.FeedForward.create(
symbol = actor_output,
X = actor_train.x,
y = actor_train.y,
ctx = device.cpu,
num.round = 100,
array.batch.size = 1,
optimizer = 'adam',
eval.metric = actor_loss,
clip_gradient = 1,
wd = 0.01,
initializer = actor_initializer,
array.layout = "rowmajor" )
predict(actor_model, actor_train.x, array.layout = "rowmajor")

Answer 1

It is quite strange to me, but I found a solution.
I changed optimizer from optimizer = 'adam' to optimizer = 'rmsprop', and the NN started to converge as expected in case of negative targets. I made simulations in R using a simple NN and optim function to get the same result.
Looks like adam or SGD may be buggy or whatever in case of multinomial classification... I also used to get stuck at the fact those optimizers did not converge to a perfect solution on just 1 example, while rmsprop does! Be aware!