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local StochasticGradient = torch.class('nn.StochasticGradient')
function StochasticGradient:__init(module, criterion)
self.learningRate = 0.01
self.learningRateDecay = 0
self.maxIteration = 25
self.shuffleIndices = true
self.module = module
self.criterion = criterion
self.verbose = true
end
function StochasticGradient:train(dataset)
local iteration = 1
local currentLearningRate = self.learningRate
local module = self.module
local criterion = self.criterion
local shuffledIndices = torch.randperm(dataset:size(), 'torch.LongTensor')
if not self.shuffleIndices then
for t = 1,dataset:size() do
shuffledIndices[t] = t
end
end
print("# StochasticGradient: training")
while true do
local currentError = 0
for t = 1,dataset:size() do
local example = dataset[shuffledIndices[t]]
local input = example[1]
local target = example[2]
currentError = currentError + criterion:forward(module:forward(input), target)
module:updateGradInput(input, criterion:updateGradInput(module.output, target))
module:accUpdateGradParameters(input, criterion.gradInput, currentLearningRate)
if self.hookExample then
self.hookExample(self, example)
end
end
currentError = currentError / dataset:size()
if self.hookIteration then
self.hookIteration(self, iteration, currentError)
end
if self.verbose then
print("# current error = " .. currentError)
end
iteration = iteration + 1
currentLearningRate = self.learningRate/(1+iteration*self.learningRateDecay)
if self.maxIteration > 0 and iteration > self.maxIteration then
print("# StochasticGradient: you have reached the maximum number of iterations")
print("# training error = " .. currentError)
break
end
end
end
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