# HG changeset patch
# User Yoshua Bengio <bengioy@iro.umontreal.ca>
# Date 1294626835 18000
# Node ID e162e75ac5c6250bc0b02882a68418a342a2436e
# Parent  5c67f674d724e4f2121fdf6de85a9de76224ec06# Parent  820764689d2fd9b0300b1d19eba2678767fa3412
merge

diff -r 5c67f674d724 -r e162e75ac5c6 deep/stacked_dae/aistats_review/m_mlp_ift.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/aistats_review/m_mlp_ift.py	Sun Jan 09 21:33:55 2011 -0500
@@ -0,0 +1,121 @@
+import pdb,bricks.costs,datetime,os,theano,sys
+from bricks.experiments import *
+from bricks.networks import *
+from bricks import *
+from datasets import *
+from bricks.optimizer import *
+from monitor.exp_monitoring import *
+
+#from monitor.series import *
+import numpy
+#import jobman,jobman.sql,pylearn.version
+#from jobman import DD
+#from utils.JobmanHandling import *
+
+class MnistTSdaeExperiment(ExperimentObject):
+    # Todo : Write down the interface
+
+    def _init_dataset(self):
+        self.dataset_list = [ PNIST07(), nist_all() ]
+        self.dataset = self.dataset_list[0]
+
+
+    def _init_outputs(self):
+        self.ds_output = { 'Pnist_Train' : self.dataset_list[0].train,
+                           'Pnist_Valid' : self.dataset_list[0].valid,
+                           'Pnist_Test' : self.dataset_list[0].test,
+                           'nist_Train' : self.dataset_list[1].train,
+                           'nist_Valid' : self.dataset_list[1].valid,
+                           'nist_Test' : self.dataset_list[1].test}
+
+        self.outputs = { 'CC' : costs.classification_error(self.network.layers[-1][0].out_dict['argmax_softmax_output'],self.network.in_dict['pred']) }
+                         #'L1' : costs.L1(self.network.layers[0][0].out_dict['sigmoid_output']) }
+                         #'LL' : costs.negative_ll(self.network.layers[-1][0].out_dict['softmax_output'],self.network.in_dict['pred']) }
+
+
+
+    def _init_network(self):
+        """ Choose wich network to initialize """
+        #x,y = self.dataset.train(1).next()
+        n_i = 1024
+        n_o = 62
+        numpy.random.seed(self.hp['seed'])
+        self.network = MLPNetwork(n_i,n_o,size=self.hp['size'])
+        default.load_pickled_network(self.network,'best_params/1/')
+
+    def _init_costs_params(self):
+        #finetuning
+        self.costs  = [ [costs.negative_ll(self.network.layers[-1][0].out_dict['softmax_output'],self.network.in_dict['pred'])] ]
+        self.params = [ [self.network.get_all_params(),self.network.get_all_params()] ]
+
+
+    def _init_monitor(self):
+        self.monitor = monitor(self.outputs,self.ds_output,self.network,self.sub_paths,save_criterion='Pnist_Valid')
+
+    def startexp(self):
+        print self.info()
+        for j,optimizer in enumerate(self.optimizers):
+            print 'Optim', '#'+str(j+1)
+            sys.stdout.flush()
+            for i in range(self.hp['ft_ep']):
+                optimizer.tune(self.dataset.train,self.hp['bs'])
+                print repr(i).rjust(3),self.monitor.get_str_output()
+                sys.stdout.flush()
+
+
+    def run(self):
+        self.startexp()
+        self.monitor.dump()
+        return True
+
+def jobman_entrypoint(state, channel):
+    import jobman,jobman.sql,pylearn.version
+    from jobman import DD
+    from utils.JobmanHandling import JobHandling,jobman_insert,cartesian_product_jobs
+    exp = MnistTSdaeExperiment(state,channel)
+    return exp.jobhandler.start(state,channel)
+
+def standalone(state):
+    exp = MnistTSdaeExperiment(state)
+    exp.run()
+
+
+if __name__ == '__main__':
+    HP = { 'lr':[ [ .1] ],
+           'ft_ep':[100],
+           'bs':[100],
+           'size':[ [300],[4000],[5000],[6000],[7000] ],
+           'seed':[0]}
+
+    job_db_path = 'postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift'
+    exp_path = "m_mlp_ift.jobman_entrypoint"
+
+    args = sys.argv[1:]
+
+    if len(args) > 0 and args[0] == 'jobman_insert':
+        jobman_insert(HP,job_db_path,exp_path)
+
+    elif len(args) > 0 and args[0] == 'jobman_test':
+        chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
+        dd_hp = cartesian_product_jobs(HP)
+        print dd_hp[0]
+        jobman_entrypoint(dd_hp[0], chanmock)
+
+    elif len(args) > 0 and args[0] == 'standalone':
+        hp = { 'lr':[ .1],
+           'ft_ep':100,  
+           'bs':100,
+           'size':[ 3000 ],
+           'seed':0}
+        standalone(hp)
+        
+        
+    else:
+        print "Bad arguments"
+
+
+#jobman sqlview  postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift m_mlp_ift_view
+#psql -h gershwin -U mullerx -d mullerx_db
+#b9f6ed1ee4
+
+#jobdispatch --condor  --env=THEANO_FLAGS=floatX=float32 --repeat_jobs=5 jobman sql -n0 'postgres://mullerx:b9f6ed1ee4@gershwin/mullerx_db/m_mlp_ift' .