torch - LookupTable and gradient update - machine-learning

Question

I am trying to implement a neural network with multiple layers. I am trying to understand if what I have done is correct and if not, how do I debug this. The way I do it is, I define my neural network in the following manner (I initialise the lookuptable layer with some prior learned embeddings):
lookupTableLayer = nn.LookupTable(vector:size()[1], d)
for i=1,vector:size()[1] do
lookupTableLayer.weight[i] = vector[i]
end
mlp=nn.Sequential();
mlp:add(lookupTableLayer)
mlp:add(nn.TemporalConvolution(d,H,K,dw))
mlp:add(nn.Tanh())
mlp:add(nn.Max(1))
mlp:add(nn.Tanh())
mlp:add(nn.Linear(H,d))
Now, to train the network, I loop through every training example and for every example I call gradUpdate() which has this code (this is straight from the examples):
function gradUpdate(mlp, x, indexY, learningRate)
local pred = mlp:forward(x)
local gradCriterion = findGrad(pred, indexY)
mlp:zeroGradParameters()
mlp:backward(x, gradCriterion)
mlp:updateParameters(learningRate)
end
The findGrad function is just an implementation of WARP Loss which returns the gradient wrt output. I am wondering if this is all I need? I assume this will backpropagate and update the parameters of all the layers. To check this, I trained this network and saved the model. Then I loaded the model and did:
{load saved mlp after training}
lookuptable = mlp:findModules('nn.LookupTable')[1]
Now, I checked vector[1] and lookuptable.weight[1] and they were the same. I can't understand why did the weights in the lookup table layer not get updated? What am I missing here?
Looking forward to your replies!