new_contents: from __future__ import print_function
  import sys, os
  sys.path.insert(1, os.path.join("..","..",".."))
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.deepwater import H2ODeepWaterEstimator
  
  def deepwater_lenet():
    if not H2ODeepWaterEstimator.available(): return
  
    frame = h2o.import_file(pyunit_utils.locate("bigdata/laptop/deepwater/imagenet/cat_dog_mouse.csv"))
    print(frame.head(5))
    model = H2ODeepWaterEstimator(epochs=100, learning_rate=1e-3, network='lenet', score_interval=0, train_samples_per_iteration=1000)
    model.train(x=[0],y=1, training_frame=frame)
  
    extracted = model.deepfeatures(frame, "pooling1_output")
    #print(extracted.describe())
    print(extracted.ncols)
    assert extracted.ncols == 800, "extracted frame doesn't have 800 columns"
  
    extracted = model.deepfeatures(frame, "activation2_output")
    #print(extracted.describe())
    print(extracted.ncols)
    assert extracted.ncols == 500, "extracted frame doesn't have 500 columns"
  
    h2o.remove_all()
  
  if __name__ == "__main__":
    pyunit_utils.standalone_test(deepwater_lenet)
  else:
    deepwater_lenet()
  
subject: Add PyUnit for deep feature extraction of a LeNet model with mxnet.