1# Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================== 15"""Grid search tests.""" 16 17from __future__ import absolute_import 18from __future__ import division 19from __future__ import print_function 20 21import os 22import random 23 24from tensorflow.contrib.learn.python import learn 25from tensorflow.python.platform import test 26 27HAS_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False) 28if HAS_SKLEARN: 29 try: 30 # pylint: disable=g-import-not-at-top 31 from sklearn import datasets 32 from sklearn.grid_search import GridSearchCV 33 from sklearn.metrics import accuracy_score 34 except ImportError: 35 HAS_SKLEARN = False 36 37 38class GridSearchTest(test.TestCase): 39 """Grid search tests.""" 40 41 def testIrisDNN(self): 42 if HAS_SKLEARN: 43 random.seed(42) 44 iris = datasets.load_iris() 45 feature_columns = learn.infer_real_valued_columns_from_input(iris.data) 46 classifier = learn.DNNClassifier( 47 feature_columns=feature_columns, 48 hidden_units=[10, 20, 10], 49 n_classes=3) 50 grid_search = GridSearchCV( 51 classifier, {'hidden_units': [[5, 5], [10, 10]]}, 52 scoring='accuracy', 53 fit_params={'steps': [50]}) 54 grid_search.fit(iris.data, iris.target) 55 score = accuracy_score(iris.target, grid_search.predict(iris.data)) 56 self.assertGreater(score, 0.5, 'Failed with score = {0}'.format(score)) 57 58 59if __name__ == '__main__': 60 test.main() 61