new_contents: #  Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import shutil
  
  from sklearn import datasets, metrics, cross_validation
  from tensorflow.contrib import skflow
  
  iris = datasets.load_iris()
  X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
      test_size=0.2, random_state=42)
  
  classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
  classifier.fit(X_train, y_train)
  score = metrics.accuracy_score(y_test, classifier.predict(X_test))
  print('Accuracy: {0:f}'.format(score))
  
  # Clean checkpoint folder if exists
  try:
      shutil.rmtree('/home/ubuntu/workspace/skflow_examples/iris/iris_custom_model')
  except OSError:
      pass
  
  # Save model, parameters and learned variables.
  classifier.save('/home/ubuntu/workspace/skflow-examples/iris_custom_model')
  classifier = None
  
  ## Restore everything
  new_classifier = skflow.TensorFlowEstimator.restore('/home/ubuntu/workspace/skflow-examples/iris_custom_model')
  score = metrics.accuracy_score(y_test, new_classifier.predict(X_test))
  print('Accuracy: {0:f}'.format(score))
  
subject: Create z12-iris-save-restore.py