old_contents: import convolutional_mlp, logistic_cg, logistic_sgd, mlp, SdA, dA, rbm , DBN
  from nose.plugins.skip import SkipTest
  import theano
  import time, sys
  
  
  def test_logistic_sgd():
      t0=time.time()
      logistic_sgd.sgd_optimization_mnist(n_epochs=10)
      print >> sys.stderr, "test_logistic_sgd took %.3fs expected 15.2s in our buildbot"%(time.time()-t0)
  
  
  def test_logistic_cg():
      t0=time.time()
      logistic_cg.cg_optimization_mnist(n_epochs=10)
      print >> sys.stderr, "test_logistic_cg took %.3fs expected 14s in our buildbot"%(time.time()-t0)
  
  
  def test_mlp():
      t0=time.time()
      mlp.test_mlp(n_epochs=5)
      print >> sys.stderr, "test_mlp took %.3fs expected 118s in our buildbot"%(time.time()-t0)
  
  
  def test_convolutional_mlp():
      t0=time.time()
      convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5])
      print >> sys.stderr, "test_convolutional_mlp took %.3fs expected 168s in our buildbot"%(time.time()-t0)
  
  
  
  def test_dA():
      t0=time.time()
      dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots')
      print >> sys.stderr, "test_dA took %.3fs expected Xs in our buildbot"%(time.time()-t0)
  
  
  def test_SdA():
      t0=time.time()
      SdA.test_SdA(pretraining_epochs = 2, training_epochs = 3, batch_size = 300)
      print >> sys.stderr, "test_SdA took %.3fs expected 971s in our buildbot"%(time.time()-t0)
  
  
  def test_dbn():
      t0=time.time()
      DBN.test_DBN(pretraining_epochs = 1, training_epochs = 2, batch_size =300,
              output_folder = 'tmp_DBN_plots')
      print >> sys.stderr, "test_mlp took %.3fs expected ??s in our buildbot"%(time.time()-t0)
  
  
  
  def test_rbm():
      t0=time.time()
      rbm.test_rbm(training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, 
              output_folder =  'tmp_rbm_plots')
      print >> sys.stderr, "test_rbm took %.3fs expected ??s in our buildbot"%(time.time()-t0)
  
  
  
new_contents: import convolutional_mlp, logistic_cg, logistic_sgd, mlp, SdA, dA, rbm , DBN
  from nose.plugins.skip import SkipTest
  import theano
  import time, sys
  
  
  def test_logistic_sgd():
      t0=time.time()
      logistic_sgd.sgd_optimization_mnist(n_epochs=10)
      print >> sys.stderr, "test_logistic_sgd took %.3fs expected 15.2s in our buildbot"%(time.time()-t0)
  
  
  def test_logistic_cg():
      t0=time.time()
      logistic_cg.cg_optimization_mnist(n_epochs=10)
      print >> sys.stderr, "test_logistic_cg took %.3fs expected 14s in our buildbot"%(time.time()-t0)
  
  
  def test_mlp():
      t0=time.time()
      mlp.test_mlp(n_epochs=5)
      print >> sys.stderr, "test_mlp took %.3fs expected 118s in our buildbot"%(time.time()-t0)
  
  
  def test_convolutional_mlp():
      t0=time.time()
      convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5])
      print >> sys.stderr, "test_convolutional_mlp took %.3fs expected 168s in our buildbot"%(time.time()-t0)
  
  
  
  def test_dA():
      t0=time.time()
      dA.test_dA(training_epochs = 3, output_folder = 'tmp_dA_plots')
      print >> sys.stderr, "test_dA took %.3fs expected Xs in our buildbot"%(time.time()-t0)
  
  
  def test_SdA():
      t0=time.time()
      SdA.test_SdA(pretraining_epochs = 2, training_epochs = 3, batch_size = 300)
      print >> sys.stderr, "test_SdA took %.3fs expected 971s in our buildbot"%(time.time()-t0)
  
  
  def test_dbn():
      t0=time.time()
      DBN.test_DBN(pretraining_epochs = 1, training_epochs = 2, batch_size =300)
      print >> sys.stderr, "test_mlp took %.3fs expected ??s in our buildbot"%(time.time()-t0)
  
  
  
  def test_rbm():
      t0=time.time()
      rbm.test_rbm(training_epochs = 1, batch_size = 300, n_chains = 1, n_samples = 1, 
              output_folder =  'tmp_rbm_plots')
      print >> sys.stderr, "test_rbm took %.3fs expected ??s in our buildbot"%(time.time()-t0)
  
  
  
subject: fix fct call in test.