tensorflow_estimator/python/estimator/canned/dnn_test_fc_v2.py [193:304]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn( feature_spec) export_dir = est.export_saved_model(tempfile.mkdtemp(), serving_input_receiver_fn) self.assertTrue(tf.compat.v1.gfile.Exists(export_dir)) def test_numpy_input_fn(self): """Tests complete flow with numpy_input_fn.""" label_dimension = 2 batch_size = 10 data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32) data = data.reshape(batch_size, label_dimension) # learn y = x train_input_fn = numpy_io.numpy_input_fn( x={'x': data}, y=data, batch_size=batch_size, num_epochs=None, shuffle=True) eval_input_fn = numpy_io.numpy_input_fn( x={'x': data}, y=data, batch_size=batch_size, shuffle=False) predict_input_fn = numpy_io.numpy_input_fn( x={'x': data}, batch_size=batch_size, shuffle=False) self._test_complete_flow( train_input_fn=train_input_fn, eval_input_fn=eval_input_fn, predict_input_fn=predict_input_fn, input_dimension=label_dimension, label_dimension=label_dimension, batch_size=batch_size) def test_pandas_input_fn(self): """Tests complete flow with pandas_input_fn.""" if not HAS_PANDAS: return label_dimension = 1 batch_size = 10 data = np.linspace(0., 2., batch_size, dtype=np.float32) x = pd.DataFrame({'x': data}) y = pd.Series(data) train_input_fn = pandas_io.pandas_input_fn( x=x, y=y, batch_size=batch_size, num_epochs=None, shuffle=True) eval_input_fn = pandas_io.pandas_input_fn( x=x, y=y, batch_size=batch_size, shuffle=False) predict_input_fn = pandas_io.pandas_input_fn( x=x, batch_size=batch_size, shuffle=False) self._test_complete_flow( train_input_fn=train_input_fn, eval_input_fn=eval_input_fn, predict_input_fn=predict_input_fn, input_dimension=label_dimension, label_dimension=label_dimension, batch_size=batch_size) def test_input_fn_from_parse_example(self): """Tests complete flow with input_fn constructed from parse_example.""" label_dimension = 2 batch_size = 10 data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32) data = data.reshape(batch_size, label_dimension) serialized_examples = [] for datum in data: example = example_pb2.Example( features=feature_pb2.Features( feature={ 'x': feature_pb2.Feature( float_list=feature_pb2.FloatList(value=datum)), 'y': feature_pb2.Feature( float_list=feature_pb2.FloatList(value=datum)), })) serialized_examples.append(example.SerializeToString()) feature_spec = { 'x': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32), 'y': tf.io.FixedLenFeature([label_dimension], tf.dtypes.float32), } def _train_input_fn(): feature_map = tf.compat.v1.io.parse_example(serialized_examples, feature_spec) features = _queue_parsed_features(feature_map) labels = features.pop('y') return features, labels def _eval_input_fn(): feature_map = tf.compat.v1.io.parse_example( tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1), feature_spec) features = _queue_parsed_features(feature_map) labels = features.pop('y') return features, labels def _predict_input_fn(): feature_map = tf.compat.v1.io.parse_example( tf.compat.v1.train.limit_epochs(serialized_examples, num_epochs=1), feature_spec) features = _queue_parsed_features(feature_map) features.pop('y') return features, None self._test_complete_flow( train_input_fn=_train_input_fn, eval_input_fn=_eval_input_fn, predict_input_fn=_predict_input_fn, input_dimension=label_dimension, label_dimension=label_dimension, batch_size=batch_size) - 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