diff --git a/train.lua b/train.lua
index 790df96..da6a72a 100644
--- a/train.lua
+++ b/train.lua
@@ -36,6 +36,8 @@ cmd:option('-model', 'model', 'neural network model')
 cmd:option('-data', 'data.t7', 'training data')
 cmd:option('-learningRate', 0.01, 'learning rate')
 cmd:option('-initweights', '', 'initial weights')
+cmd:option('-initstate', '', 'initial optimizer state')
+
 
 params = cmd:parse(arg)
 
@@ -144,13 +146,20 @@ function trainModel(weights)
 	return {cost}, dw
 end
 
--- create directory to save weights and videos
+-- create directory to save weights, optimizer state and videos
 lfs.mkdir('weights_' .. params.model)
+lfs.mkdir('state_'   .. params.model)
 lfs.mkdir('video_'   .. params.model)
 
 local total_cost, config, state = 0, { learningRate = params.learningRate }, {}
 collectgarbage()
 
+if #params.initstate > 0 then
+	print('Loading optimizer state ' .. params.initstate)
+	state=torch.load(params.initstate)
+end
+
+
 for k = 1,params.iter do
 	xlua.progress(k, params.iter)
 	local _, cost = optim.adagrad(trainModel, w, config, state)
@@ -159,8 +168,10 @@ for k = 1,params.iter do
 	if k % 1000 == 0 then
 		print('Iteration ' .. k .. ', cost: ' .. total_cost / 1000)
 		total_cost = 0
-		-- save weights
+		-- save weights and optimizer state
 		torch.save('weights_' .. params.model .. '/' .. k .. '.dat', w:type('torch.FloatTensor'))
+		torch.save('state_' .. params.model .. '/' .. k .. '.dat', state)
+		
 		-- visualise performance
 		evalModel(w)
 	end