tensorflow: DNNClassifier estimator cannot be exported

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System information

tensorflow/tensorflow:latest container
ubuntu linux
installed from pip
TensorFlow version ‘v1.2.0-5-g435cdfc’, ‘1.2.1’:
Python 2.7:
Bazel version (if compiling from source):

Exact command to reproduce

classifier = DNNClassifier(feature_columns=feature_columns,
                         hidden_units=[10, 20, 10],
                         n_classes=3,
                         model_dir=model_path)

classifier.export_savedmodel(MODEL_PATH, script.serving_input_receiver_fn)

Describe the problem

Trying to export the model DNNClassifier throws the exception:

Exception during training: A default input_alternative must be provided.
 Traceback (most recent call last):
  File "algo.py", line 78, in train
    nn.export_savedmodel(MODEL_PATH, script.serving_input_receiver_fn, default_output_alternative_key=None)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1280, in export_savedmodel
    actual_default_output_alternative_key)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py", line 259, in build_all_signature_defs
    raise ValueError('A default input_alternative must be provided.')

The problem happens because DNNClassifier constructor creates a head with name None: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/estimators/dnn.py#L365

About this issue

Original URL
State: closed
Created 7 years ago
Reactions: 2
Comments: 25 (10 by maintainers)

Links to this issue

python - сохранить модель tensorflow( tf.estimator.Estimator ) - Stack Overflow на русском

Most upvoted comments

Hello @samithaj

Canned estimator don’t have a lot of documentation yet. Here it go my code:

INPUT_TENSOR_NAME = 'inputs'


def estimator(model_path):
    feature_columns = [tf.feature_column.numeric_column(INPUT_TENSOR_NAME, shape=[4])]
    return tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                      hidden_units=[10, 20, 10],
                                      n_classes=3,
                                      model_dir=model_path)


def serving_input_receiver_fn():
    feature_spec = {INPUT_TENSOR_NAME: tf.FixedLenFeature(dtype=tf.float32, shape=[4])}
    return tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)()


def train_input_fn(training_dir):
    training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=os.path.join(training_dir, 'iris_training.csv'),
        target_dtype=np.int,
        features_dtype=np.float32)

    return tf.estimator.inputs.numpy_input_fn(
        x={INPUT_TENSOR_NAME: np.array(training_set.data)},
        y=np.array(training_set.target),
        num_epochs=None,
        shuffle=True)()

Hope it helps you!

mvsusp on Aug 28, 2017

Cool, @mvsusp .

By the way, if tf.feature_column is used, feature_spec can be generated automatically, like:

    feature_spec = tf.feature_column.make_parse_example_spec(feature_columns)

facaiy on Aug 29, 2017

Hi @facaiy

The issue was solved using tf.estimator.export.build_parsing_serving_input_receiver_fn.

Thank you!

mvsusp on Aug 28, 2017

@Anmol-Sharma I guess you need to return export_outputs in your model_fn, like

        return tf.estimator.EstimatorSpec(
            mode=mode,
            predictions=predictions,
            loss=loss,
            train_op=train_op,
            export_outputs=export_outputs)

felicitywang on Dec 11, 2017

@AKhilGarg91

I got the too many values to unpack on 1.4, too.

a small dig into that I changed tf.estimator.export.build_parsing_serving_input_receiver_fn to tensorflow.contrib.learn.build_parsing_serving_input_fn

In tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py line 157-161

  if isinstance(input_ops, input_fn_utils.InputFnOps):    
    features, unused_labels, default_inputs = input_ops #<- should go here
    input_alternatives[DEFAULT_INPUT_ALTERNATIVE_KEY] = default_inputs
  else:
    features, unused_labels = input_ops #<- this line fails

In InputFnOps, it says

Contents of this file are moved to tensorflow/python/estimator/export.py.
InputFnOps is renamed to ServingInputReceiver.
build_parsing_serving_input_fn is renamed to
  build_parsing_serving_input_receiver_fn.
build_default_serving_input_fn is renamed to
  build_raw_serving_input_receiver_fn.

Seems the new class causes the error.

if isinstance(input_ops, input_fn_utils.InputFnOps):

should change to something like

if isinstance(input_ops, input_fn_utils.InputFnOps) or isinstance(input_ops, export.ServingInputReceiver):

My work around is to use tensorflow.contrib.learn.build_parsing_serving_input_fn instead for now.

twksos on Dec 1, 2017

thanks @mvsusp Check here anyone looking for complete example: https://github.com/MtDersvan/tf_playground

samithaj on Aug 30, 2017

@mvsusp Can you please post the fixed code for your example ,I’m trying tho export and serve a DNNLinearCombinedRegressor model , and i cant find any working example

samithaj on Aug 28, 2017