Feeding example to tf predictor.from_saved_model() for estimator trained with tf hub module - tensorflow

Question

I try to export the model for text classification with tf hub modules, and then infer a prediction from it for a single string example using predictor.from_saved_model(). I saw some examples of similar ideas, but still couldn't make it work for the case when using tf hub modules to build features. Here is what I do:
train_input_fn = tf.estimator.inputs.pandas_input_fn(
train_df, train_df['label_ids'], num_epochs= None, shuffle=True)
# Prediction on the whole training set.
predict_train_input_fn = tf.estimator.inputs.pandas_input_fn(
train_df, train_df['label_ids'], shuffle=False)
embedded_text_feature_column = hub.text_embedding_column(
key='sentence',
module_spec='https://tfhub.dev/google/nnlm-de-dim128/1')
#Estimator
estimator = tf.estimator.DNNClassifier(
hidden_units=[500, 100],
feature_columns=[embedded_text_feature_column],
n_classes=num_of_class,
optimizer=tf.train.AdagradOptimizer(learning_rate=0.003) )
# Training
estimator.train(input_fn=train_input_fn, steps=1000)
#prediction on training set
train_eval_result = estimator.evaluate(input_fn=predict_train_input_fn)
print('Training set accuracy: {accuracy}'.format(**train_eval_result))
feature_spec = tf.feature_column.make_parse_example_spec([embedded_text_feature_column])
serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
export_dir_base = self.cfg['model_path']
servable_model_path = estimator.export_savedmodel(export_dir_base, serving_input_receiver_fn)
# Example message for inference
message = "Was ist denn los"
saved_model_predictor = predictor.from_saved_model(export_dir=servable_model_path)
content_tf_list = tf.train.BytesList(value=[str.encode(message)])
example = tf.train.Example(
features=tf.train.Features(
feature={
'sentence': tf.train.Feature(
bytes_list=content_tf_list
)
}
)
)
with tf.python_io.TFRecordWriter('the_message.tfrecords') as writer:
writer.write(example.SerializeToString())
reader = tf.TFRecordReader()
data_path = 'the_message.tfrecords'
filename_queue = tf.train.string_input_producer([data_path], num_epochs=1)
_, serialized_example = reader.read(filename_queue)
output_dict = saved_model_predictor({'inputs': [serialized_example]})
And the output:
Traceback (most recent call last):
File "/Users/dimitrs/component-pythia/src/pythia.py", line 321, in _train
model = algo.generate_model(samples, generation_id)
File "/Users/dimitrs/component-pythia/src/algorithm_layer/algorithm.py", line 56, in generate_model
model = self._process_training(samples, generation)
File "/Users/dimitrs/component-pythia/src/algorithm_layer/tf_hub_classifier.py", line 91, in _process_training
output_dict = saved_model_predictor({'inputs': [serialized_example]})
File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/contrib/predictor/predictor.py", line 77, in __call__
return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 900, in run
run_metadata_ptr)
File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1316, in _do_run
run_metadata)
File "/Users/dimitrs/anaconda3/envs/pythia/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unable to get element as bytes.
Isn't serialized_example the right input that is suggested by serving_input_receiver_fn ?

Answer 1

So, all I need was serialized_example = example.SerializeToString()
Writing the example on a file requires to start a session before reading it back. Simply serialising is enough:
# Example message for inference
message = "Was ist denn los"
saved_model_predictor = predictor.from_saved_model(export_dir=servable_model_path)
content_tf_list = tf.train.BytesList(value=[message.encode('utf-8')])
sentence = tf.train.Feature(bytes_list=content_tf_list)
sentence_dict = {'sentence': sentence}
features = tf.train.Features(feature=sentence_dict)
example = tf.train.Example(features=features)
serialized_example = example.SerializeToString()
output_dict = saved_model_predictor({'inputs': [serialized_example]})