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 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 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 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)