updated logistic sgd outputs

Author: pascanur

code/logistic_sgd.py

@@ -298,48 +298,49 @@ def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000, dataset='../data/m
     done_looping = False 
     epoch = 0  
     while (epoch < n_epochs) and (not done_looping):
-      epoch = epoch + 1
-      for minibatch_index in xrange(n_train_batches):
+        epoch = epoch + 1
+        for minibatch_index in xrange(n_train_batches):
 
-        minibatch_avg_cost = train_model(minibatch_index)
-        # iteration number
-        iter = epoch * n_train_batches + minibatch_index
+            minibatch_avg_cost = train_model(minibatch_index)
+            # iteration number
+            iter = epoch * n_train_batches + minibatch_index
 
-        if (iter+1) % validation_frequency == 0: 
-            # compute zero-one loss on validation set 
-            validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
-            this_validation_loss = numpy.mean(validation_losses)
+            if (iter+1) % validation_frequency == 0: 
+                # compute zero-one loss on validation set 
+                validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
+                this_validation_loss = numpy.mean(validation_losses)
 
-            print('epoch %i, minibatch %i/%i, validation error %f %%' % \
-                 (epoch, minibatch_index+1,n_train_batches, \
-                  this_validation_loss*100.))
+                print('epoch %i, minibatch %i/%i, validation error %f %%' % \
+                    (epoch, minibatch_index+1,n_train_batches, \
+                    this_validation_loss*100.))
 
 
-            # if we got the best validation score until now
-            if this_validation_loss < best_validation_loss:
-                #improve patience if loss improvement is good enough
-                if this_validation_loss < best_validation_loss *  \
+                # if we got the best validation score until now
+                if this_validation_loss < best_validation_loss:
+                    #improve patience if loss improvement is good enough
+                    if this_validation_loss < best_validation_loss *  \
                        improvement_threshold :
-                    patience = max(patience, iter * patience_increase)
+                        patience = max(patience, iter * patience_increase)
 
-                best_validation_loss = this_validation_loss
-                # test it on the test set
+                    best_validation_loss = this_validation_loss
+                    # test it on the test set
 
-                test_losses = [test_model(i) for i in xrange(n_test_batches)]
-                test_score  = numpy.mean(test_losses)
+                    test_losses = [test_model(i) for i in xrange(n_test_batches)]
+                    test_score  = numpy.mean(test_losses)
 
-                print(('     epoch %i, minibatch %i/%i, test error of best ' 
+                    print(('     epoch %i, minibatch %i/%i, test error of best ' 
                        'model %f %%') % \
-                  (epoch, minibatch_index+1, n_train_batches,test_score*100.))
+                        (epoch, minibatch_index+1, n_train_batches,test_score*100.))
 
-        if patience <= iter :
+            if patience <= iter :
                 done_looping = True
                 break
 
     end_time = time.clock()
     print(('Optimization complete with best validation score of %f %%,'
            'with test performance %f %%') %  
                  (best_validation_loss * 100., test_score*100.))
+    print 'The code run for %d epochs, with %f epochs/sec'%(epoch,1.*(end_time-start_time)/epoch)
     print >> sys.stderr, ('The code for file '+os.path.split(__file__)[1]+' ran for %.1fs expected 3.8s in our buildbot' % ((end_time-start_time)))
 
 if __name__ == '__main__':