From e7024d5b41e5efa9ef36b20fe8fbf5b93df25667 Mon Sep 17 00:00:00 2001
From: Clement Farabet <clement.farabet@gmail.com>
Date: Wed, 24 Aug 2011 11:39:52 -0400
Subject: More complete hook system for Optimizations

---
 LBFGSOptimization.lua | 11 ++++++++---
 SGDOptimization.lua   | 13 ++++++++++---
 2 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/LBFGSOptimization.lua b/LBFGSOptimization.lua
index f901a57..671ce7a 100644
--- a/LBFGSOptimization.lua
+++ b/LBFGSOptimization.lua
@@ -13,7 +13,8 @@ function LBFGS:__init(...)
    self.gradParametersT = nnx.getGradParameters(self.module)
 end
 
-function LBFGS:forward(inputs, targets)
+function LBFGS:forward(inputs, targets, options)
+   options = options or {}
    -- (1) construct a closure that compute f(inputs) + df/dW
    --     after each call to that function:
    --       + self.parameters contains the current X vector
@@ -29,6 +30,10 @@ function LBFGS:forward(inputs, targets)
            self.output = 0
            -- given all inputs, evaluate gradients
            for i = 1,#inputs do
+              -- user hook
+              if self.prehook then
+                 self.prehook(self, {inputs[i], targets[i], options[i]})
+              end
               -- estimate f
               local output = self.module:forward(inputs[i])
               local err = self.criterion:forward(output, targets[i])
@@ -37,8 +42,8 @@ function LBFGS:forward(inputs, targets)
               local df_do = self.criterion:backward(output, targets[i])
               self.module:backward(inputs[i], df_do)
               -- user hook
-              if self.hook then
-                 self.hook(self, {inputs[i], targets[i]})
+              if self.posthook then
+                 self.posthook(self, {inputs[i], targets[i], options[i]})
               end
            end
            -- update state from computed parameters
diff --git a/SGDOptimization.lua b/SGDOptimization.lua
index 52d780e..f59dc02 100644
--- a/SGDOptimization.lua
+++ b/SGDOptimization.lua
@@ -14,7 +14,9 @@ function SGD:__init(...)
    self.gradParametersT = nnx.getGradParameters(self.module)
 end
 
-function SGD:forward(inputs, targets)
+function SGD:forward(inputs, targets, options)
+   options = options or {}
+
    -- reset gradients
    self.module:zeroGradParameters()
    
@@ -23,6 +25,11 @@ function SGD:forward(inputs, targets)
    
    -- given all inputs, evaluate gradients
    for i = 1,#inputs do
+      -- user hook
+      if self.prehook then
+         self.prehook(self, {inputs[i], targets[i], options[i]})
+      end
+
       -- estimate f
       local output = self.module:forward(inputs[i])
       local err = self.criterion:forward(output, targets[i])
@@ -33,8 +40,8 @@ function SGD:forward(inputs, targets)
       self.module:backward(inputs[i], df_do)
 
       -- user hook
-      if self.hook then
-         self.hook(self, {inputs[i], targets[i]})
+      if self.posthook then
+         self.posthook(self, {inputs[i], targets[i], options[i]})
       end
    end
 
-- 
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