Adds integration tests for DNNClassifier.
PiperOrigin-RevId: 157592010
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@ -695,6 +695,169 @@ class DNNRegressorIntegrationTest(test.TestCase):
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batch_size=batch_size)
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class DNNClassifierIntegrationTest(test.TestCase):
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def setUp(self):
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self._model_dir = tempfile.mkdtemp()
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def tearDown(self):
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if self._model_dir:
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shutil.rmtree(self._model_dir)
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def _test_complete_flow(
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self, train_input_fn, eval_input_fn, predict_input_fn, input_dimension,
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n_classes, batch_size):
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feature_columns = [
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feature_column.numeric_column('x', shape=(input_dimension,))]
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est = dnn.DNNClassifier(
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hidden_units=(2, 2),
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feature_columns=feature_columns,
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n_classes=n_classes,
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model_dir=self._model_dir)
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# TRAIN
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num_steps = 10
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est.train(train_input_fn, steps=num_steps)
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# EVALUTE
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scores = est.evaluate(eval_input_fn)
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self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
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self.assertIn('loss', six.iterkeys(scores))
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# PREDICT
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predicted_proba = np.array([
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x[prediction_keys.PredictionKeys.PROBABILITIES]
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for x in est.predict(predict_input_fn)
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])
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self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
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# EXPORT
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feature_spec = feature_column.make_parse_example_spec(feature_columns)
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serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
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feature_spec)
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export_dir = est.export_savedmodel(tempfile.mkdtemp(),
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serving_input_receiver_fn)
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self.assertTrue(gfile.Exists(export_dir))
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def test_numpy_input_fn(self):
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"""Tests complete flow with numpy_input_fn."""
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n_classes = 2
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input_dimension = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
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x_data = data.reshape(batch_size, input_dimension)
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y_data = np.reshape(data[:batch_size], (batch_size, 1))
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# learn y = x
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train_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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y=y_data,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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y=y_data,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = numpy_io.numpy_input_fn(
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x={'x': x_data},
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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def test_pandas_input_fn(self):
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"""Tests complete flow with pandas_input_fn."""
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if not HAS_PANDAS:
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return
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input_dimension = 1
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n_classes = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size, dtype=np.float32)
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x = pd.DataFrame({'x': data})
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y = pd.Series(data)
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train_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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num_epochs=None,
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shuffle=True)
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eval_input_fn = pandas_io.pandas_input_fn(
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x=x,
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y=y,
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batch_size=batch_size,
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shuffle=False)
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predict_input_fn = pandas_io.pandas_input_fn(
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x=x,
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batch_size=batch_size,
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shuffle=False)
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self._test_complete_flow(
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train_input_fn=train_input_fn,
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eval_input_fn=eval_input_fn,
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predict_input_fn=predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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def test_input_fn_from_parse_example(self):
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"""Tests complete flow with input_fn constructed from parse_example."""
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input_dimension = 2
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n_classes = 2
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batch_size = 10
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data = np.linspace(0., 2., batch_size * input_dimension, dtype=np.float32)
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data = data.reshape(batch_size, input_dimension)
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serialized_examples = []
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for datum in data:
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example = example_pb2.Example(features=feature_pb2.Features(
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feature={
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'x': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum)),
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'y': feature_pb2.Feature(
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float_list=feature_pb2.FloatList(value=datum[:1])),
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}))
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serialized_examples.append(example.SerializeToString())
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feature_spec = {
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'x': parsing_ops.FixedLenFeature([input_dimension], dtypes.float32),
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'y': parsing_ops.FixedLenFeature([1], dtypes.float32),
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}
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def _train_input_fn():
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feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
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features = _queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _eval_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = _queue_parsed_features(feature_map)
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labels = features.pop('y')
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return features, labels
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def _predict_input_fn():
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feature_map = parsing_ops.parse_example(
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input_lib.limit_epochs(serialized_examples, num_epochs=1),
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feature_spec)
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features = _queue_parsed_features(feature_map)
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features.pop('y')
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return features, None
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self._test_complete_flow(
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train_input_fn=_train_input_fn,
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eval_input_fn=_eval_input_fn,
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predict_input_fn=_predict_input_fn,
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input_dimension=input_dimension,
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n_classes=n_classes,
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batch_size=batch_size)
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def _full_var_name(var_name):
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return '%s/part_0:0' % var_name
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