# HG changeset patch
# User sylvainpl
# Date 1271356860 14400
# Node ID a79db7cee0355e25f370b08836e5d0512056dd50
# Parent  5ddb1878dfbcbd6bddbd17734abedc540413bdeb
Arrange pour avoir un taux d'apprentissage decroissant decent pour NIST

diff -r 5ddb1878dfbc -r a79db7cee035 deep/stacked_dae/v_sylvain/sgd_optimization.py
--- a/deep/stacked_dae/v_sylvain/sgd_optimization.py	Thu Apr 15 12:53:03 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/sgd_optimization.py	Thu Apr 15 14:41:00 2010 -0400
@@ -204,7 +204,7 @@
         parameters_finetune=[]
         
         if ind_test == 21:
-            learning_rate = self.hp.finetuning_lr / 10.0
+            learning_rate = self.hp.finetuning_lr / 5.0
         else:
             learning_rate = self.hp.finetuning_lr  #The initial finetune lr
 
@@ -295,7 +295,8 @@
                     break
             
             if decrease == 1:
-                learning_rate /= 2 #divide the learning rate by 2 for each new epoch
+		if (ind_test == 21 & epoch % 100 == 0) | ind_test == 20:
+			learning_rate /= 2 #divide the learning rate by 2 for each new epoch of P07 (or 100 of NIST)
             
             self.series['params'].append((epoch,), self.classifier.all_params)