Got rid of StochasticTrainer.

4 files changed, 1 insertions, 268 deletions

diff --git a/OnlineTrainer.lua b/OnlineTrainer.lua
index bb120e8..db662e5 100644
--- a/OnlineTrainer.lua
+++ b/OnlineTrainer.lua
@@ -74,7 +74,7 @@ function OnlineTrainer:train(dataset)
 
    while true do
       print('<trainer> on training set:')
-      print("<trainer> online epoch # " .. self.epoch .. '[batchSize = ' .. self.batchSize .. ']')
+      print("<trainer> online epoch # " .. self.epoch .. ' [batchSize = ' .. self.batchSize .. ']')
 
       self.time = sys.clock()
       self.currentError = 0
diff --git a/StochasticTrainer.lua b/StochasticTrainer.lua
deleted file mode 100644
index 62fb670..0000000
--- a/StochasticTrainer.lua
+++ /dev/null
@@ -1,265 +0,0 @@
-local StochasticTrainer, parent = torch.class('nn.StochasticTrainer','nn.Trainer')
-
-function StochasticTrainer:__init(...)
-   parent.__init(self)
-   -- unpack args
-   xlua.unpack_class(self, {...},
-      'StochasticTrainer', 
-
-      'A general-purpose stochastic trainer class.\n'
-         .. 'Provides 4 user hooks to perform extra work after each sample, or each epoch:\n'
-         .. '> trainer = nn.StochasticTrainer(...) \n'
-         .. '> trainer.hookTrainSample = function(trainer, sample) ... end \n'
-         .. '> trainer.hookTrainEpoch = function(trainer) ... end \n'
-         .. '> trainer.hookTestSample = function(trainer, sample) ... end \n'
-         .. '> trainer.hookTestEpoch = function(trainer) ... end \n'
-         .. '> ',
-
-      {arg='module', type='nn.Module', help='a module to train', req=true},
-      {arg='criterion', type='nn.Module', help='a criterion to estimate the error'},
-      {arg='preprocessor', type='nn.Module', help='a preprocessor to prime the data before the module'},
-
-      {arg='learningRate', type='number', help='learning rate (W = W - rate*dE/dW)', default=1e-2},
-      {arg='learningRateDecay', type='number', help='learning rate decay (rate = rate * (1-decay), at each epoch)', default=0},
-      {arg='weightDecay', type='number', help='amount of weight decay (W = W - decay*W)', default=0},
-      {arg='momentum', type='number', help='amount of momentum on weights (dE/W = dE/dW + momentum*prev(dE/dW))', default=0},
-      {arg='maxEpoch', type='number', help='maximum number of epochs', default=50},
-
-      {arg='maxTarget', type='boolean', help='replaces an CxHxW target map by a HxN target of max values (for NLL criterions)', default=false},
-      {arg='dispProgress', type='boolean', help='display a progress bar during training/testing', default=true},
-      {arg='skipUniformTargets', type='boolean', help='skip uniform (flat) targets during training', default=false},
-
-      {arg='save', type='string', help='path to save networks and log training'},
-      {arg='timestamp', type='boolean', help='if true, appends a timestamp to each network saved', default=false}
-   )
-   -- instantiate SGD optimization module
-   self.optimizer = nn.SGDOptimization(self.learningRate, self.weightDecay, self.momentum)
-   -- private params
-   self.errorArray = self.skipUniformTargets
-   self.trainOffset = 0
-   self.testOffset = 0
-end
-
-function StochasticTrainer:log()
-   -- save network
-   local filename = self.save
-   os.execute('mkdir -p ' .. sys.dirname(filename))
-   if self.timestamp then
-      -- use a timestamp to store all networks uniquely
-      filename = filename .. '-' .. os.date("%Y_%m_%d_%X")
-   else
-      -- if no timestamp, just store the previous one
-      if sys.filep(filename) then
-         os.execute('mv ' .. filename .. ' ' .. filename .. '.old')
-      end
-   end
-   print('<trainer> saving network to '..filename)
-   local file = torch.DiskFile(filename,'w')
-   self.module:write(file)
-   file:close()
-end
-
-function StochasticTrainer:train(dataset)
-   self.epoch = self.epoch or 1
-   local currentLearningRate = self.learningRate
-   local module = self.module
-   local criterion = self.criterion
-   self.trainset = dataset
-
-   local shuffledIndices = {}
-   if not self.shuffleIndices then
-      for t = 1,dataset:size() do
-         shuffledIndices[t] = t
-      end
-   else
-      shuffledIndices = lab.randperm(dataset:size())
-   end
-
-   local parameters = nnx.getParameters(module)
-   local gradParameters = nnx.getGradParameters(module)
-
-   while true do
-      print('<trainer> on training set:')
-      print("<trainer> stochastic gradient descent epoch # " .. self.epoch)
-
-      module:zeroGradParameters()
-
-      self.time = sys.clock()
-      self.currentError = 0
-      for t = 1,dataset:size() do
-         -- disp progress
-         if self.dispProgress then
-            xlua.progress(t, dataset:size())
-         end
-
-         -- load new sample
-         local sample = dataset[self.trainOffset + shuffledIndices[t]]
-         local input = sample[1]
-         local target = sample[2]
-         local sample_x = sample.x
-         local sample_y = sample.y
-
-         -- get max of target ?
-         if self.maxTarget then
-            target = torch.Tensor(target:nElement()):copy(target)
-            _,target = lab.max(target)
-            target = target[1]
-         end
-
-         -- is target uniform ?
-         local isUniform = false
-         if self.errorArray and target:min() == target:max() then
-            isUniform = true
-         end
-
-         -- perform SGD step
-         if not (self.skipUniformTargets and isUniform) then
-            -- optional preprocess
-            if self.preprocessor then input = self.preprocessor:forward(input) end
-
-            -- forward through model and criterion 
-            -- (if no criterion, it is assumed to be contained in the model)
-            local modelOut, error
-            if criterion then
-               modelOut = module:forward(input)
-               error = criterion:forward(modelOut, target)
-            else
-               modelOut, error = module:forward(input, target, sample_x, sample_y)
-            end
-
-            -- accumulate error
-            self.currentError = self.currentError + error
-
-            -- reset gradients
-            module:zeroGradParameters()
-
-            -- backward through model
-            -- (if no criterion, it is assumed that derror is internally generated)
-            if criterion then
-               local derror = criterion:backward(module.output, target)
-               module:backward(input, derror)
-            else
-               module:backward(input)
-            end
-
-            -- update parameters in the model
-            self.optimizer:forward(parameters, gradParameters)
-         end
-
-         -- call user hook, if any
-         if self.hookTrainSample then
-            self.hookTrainSample(self, sample)
-         end
-      end
-
-      self.currentError = self.currentError / dataset:size()
-      print("<trainer> current error = " .. self.currentError)
-
-      self.time = sys.clock() - self.time
-      self.time = self.time / dataset:size()
-      print("<trainer> time to learn 1 sample = " .. (self.time*1000) .. 'ms')
-
-      if self.hookTrainEpoch then
-         self.hookTrainEpoch(self)
-      end
-
-      if self.save then self:log() end
-
-      self.epoch = self.epoch + 1
-      currentLearningRate = self.learningRate/(1+self.epoch*self.learningRateDecay)
-      self.optimizer.learningRate = currentLearningRate
-
-      if dataset.infiniteSet then
-         self.trainOffset = self.trainOffset + dataset:size()
-      end
-
-      if self.maxEpoch > 0 and self.epoch > self.maxEpoch then
-         print("<trainer> you have reached the maximum number of epochs")
-         break
-      end
-   end
-end
-
-
-function StochasticTrainer:test(dataset)
-   print('<trainer> on testing Set:')
-
-   local module = self.module
-   local shuffledIndices = {}
-   local criterion = self.criterion
-   self.currentError = 0
-   self.testset = dataset
-
-   local shuffledIndices = {}
-   if not self.shuffleIndices then
-      for t = 1,dataset:size() do
-         shuffledIndices[t] = t
-      end
-   else
-      shuffledIndices = lab.randperm(dataset:size())
-   end
-   
-   self.time = sys.clock()
-   for t = 1,dataset:size() do
-      -- disp progress
-      if self.dispProgress then
-         xlua.progress(t, dataset:size())
-      end
-
-      -- get new sample
-      local sample = dataset[self.testOffset + shuffledIndices[t]]
-      local input = sample[1]
-      local target = sample[2]
-
-      -- max target ?
-      if self.maxTarget then
-         target = torch.Tensor(target:nElement()):copy(target)
-         _,target = lab.max(target)
-         target = target[1]
-      end
-      
-      -- test sample through current model
-      if self.preprocessor then input = self.preprocessor:forward(input) end
-      if criterion then
-         self.currentError = self.currentError + 
-	    criterion:forward(module:forward(input), target)
-      else
-         local _,error = module:forward(input, target)
-         self.currentError = self.currentError + error
-      end
-
-      -- user hook
-      if self.hookTestSample then
-         self.hookTestSample(self, sample)
-      end
-   end
-
-   self.currentError = self.currentError / dataset:size()
-   print("<trainer> test current error = " .. self.currentError)
-
-   self.time = sys.clock() - self.time
-   self.time = self.time / dataset:size()
-   print("<trainer> time to test 1 sample = " .. (self.time*1000) .. 'ms')
-
-   if self.hookTestEpoch then
-      self.hookTestEpoch(self)
-   end
-
-   if dataset.infiniteSet then
-      self.testOffset = self.testOffset + dataset:size()
-   end
-
-   return self.currentError
-end
-
-function StochasticTrainer:write(file)
-   parent.write(self,file)
-   file:writeObject(self.module)
-   file:writeObject(self.criterion)
-end
-
-function StochasticTrainer:read(file)
-   parent.read(self,file)
-   self.module = file:readObject()
-   self.criterion = file:readObject()
-end
diff --git a/init.lua b/init.lua
index 04877d3..20246bc 100644
--- a/init.lua
+++ b/init.lua
@@ -105,7 +105,6 @@ torch.include('nnx', 'LBFGSOptimization.lua')
 -- trainers:
 torch.include('nnx', 'Trainer.lua')
 torch.include('nnx', 'OnlineTrainer.lua')
-torch.include('nnx', 'StochasticTrainer.lua')
 
 -- datasets:
 torch.include('nnx', 'DataSet.lua')
diff --git a/nnx-1.0-1.rockspec b/nnx-1.0-1.rockspec
index 4529d24..cfbc571 100644
--- a/nnx-1.0-1.rockspec
+++ b/nnx-1.0-1.rockspec
@@ -83,7 +83,6 @@ build = {
          install_files(/lua/nnx SpatialCriterion.lua)
          install_files(/lua/nnx Trainer.lua)
          install_files(/lua/nnx OnlineTrainer.lua)
-         install_files(/lua/nnx StochasticTrainer.lua)
          install_files(/lua/nnx DataSet.lua)
          install_files(/lua/nnx DataList.lua)
          install_files(/lua/nnx DataSetLabelMe.lua)