# HG changeset patch
# User SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
# Date 1271876094 14400
# Node ID ffc06af1c5430b4a804648f4652104e1ecdcc855
# Parent  bc4464c0894c747b95479326cd26fd84831dab04
Ajout d'une fonctionnalite pour pouvoir avoir un taux d'apprentissage decroissant dans le pretrain

diff -r bc4464c0894c -r ffc06af1c543 deep/stacked_dae/v_sylvain/nist_sda.py
--- a/deep/stacked_dae/v_sylvain/nist_sda.py	Wed Apr 21 14:51:14 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/nist_sda.py	Wed Apr 21 14:54:54 2010 -0400
@@ -55,6 +55,11 @@
         decrease_lr = state['decrease_lr']
     else :
         decrease_lr = 0
+        
+    if state.has_key('decrease_lr_pretrain'):
+        dec=state['decrease_lr_pretrain']
+    else :
+        dec=0
  
     n_ins = 32*32
     n_outs = 62 # 10 digits, 26*2 (lower, capitals)
@@ -87,7 +92,7 @@
     nb_file=0
     if state['pretrain_choice'] == 0:
         print('\n\tpretraining with NIST\n')
-        optimizer.pretrain(datasets.nist_all()) 
+        optimizer.pretrain(datasets.nist_all(), decrease = dec) 
     elif state['pretrain_choice'] == 1:
         #To know how many file will be used during pretraining
         nb_file = int(state['pretraining_epochs_per_layer']) 
@@ -97,7 +102,7 @@
             "You have to correct the code (and be patient, P07 is huge !!)\n"+
              "or reduce the number of pretraining epoch to run the code (better idea).\n")
         print('\n\tpretraining with P07')
-        optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file)) 
+        optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file),decrease = dec) 
     channel.save()
     
     #Set some of the parameters used for the finetuning