def testBoolInputFn(self):
   with self.assertRaisesRegexp(
       ValueError, 'on integer or non floating types are not supported'):
     # pylint: disable=g-long-lambda
     estimator.infer_real_valued_columns_from_input_fn(
         lambda: (constant_op.constant(False, shape=[7, 8], dtype=dtypes.bool),
                  None))

 def testStringInputFn(self):
   with self.assertRaisesRegexp(
       ValueError, 'on integer or non floating types are not supported'):
     # pylint: disable=g-long-lambda
     estimator.infer_real_valued_columns_from_input_fn(
         lambda: (
             constant_op.constant([['%d.0' % i for i in xrange(8)] for _ in xrange(7)]),
             None))

 def testIrisInputFn(self):
   feature_columns = estimator.infer_real_valued_columns_from_input_fn(
       iris_input_fn)
   self._assert_single_feature_column([_IRIS_INPUT_DIM], dtypes.float64,
                                      feature_columns)

 def testBostonInputFn(self):
   feature_columns = estimator.infer_real_valued_columns_from_input_fn(
       boston_input_fn)
   self._assert_single_feature_column([_BOSTON_INPUT_DIM], dtypes.float64,
                                      feature_columns)

 def testFloat64InputFn(self):
   feature_columns = estimator.infer_real_valued_columns_from_input_fn(
       lambda: (array_ops.ones(shape=[7, 8], dtype=dtypes.float64), None))
   self._assert_single_feature_column([8], dtypes.float64, feature_columns)

 def testTrain(self):
   feature_columns = estimator.infer_real_valued_columns_from_input_fn(
       boston_input_fn)
   est = dnn.DNNRegressor(feature_columns=feature_columns, hidden_units=[3, 3])
   est.fit(input_fn=boston_input_fn, steps=1)
   _ = est.evaluate(input_fn=boston_input_fn, steps=1)