'InputFnOps' object has no attribute 'receiver_tensors'

[dwujellyfish]

System information

==TensorFlow installed from (source or binary)==
Source
== Python version ==
Python 2.7.13
== cat /etc/issue ==
Linux orion 4.9.0-3-amd64 #1 SMP Debian 4.9.30-2+deb9u5 (2017-09-19) x86_64 GNU/Linux
VERSION_ID="9"
VERSION="9 (stretch)"
== are we in docker ==
No
== compiler ==
c++ (Debian 6.3.0-18) 6.3.0 20170516
Copyright (C) 2016 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
== uname -a ==
Linux orion 4.9.0-3-amd64 #1 SMP Debian 4.9.30-2+deb9u5 (2017-09-19) x86_64 GNU/Linux
== check pips ==
numpy (1.12.1)
protobuf (3.5.1)
tensorflow (1.3.0)
tensorflow-tensorboard (0.1.8)
tensorflow-transform (0.3.1)
== check for virtualenv ==
True
== tensorflow import ==
tf.VERSION = 1.3.0
tf.GIT_VERSION = v1.3.0-rc2-20-g0787eee
tf.COMPILER_VERSION = v1.3.0-rc2-20-g0787eee
Sanity check: array([1], dtype=int32)
== env ==
LD_LIBRARY_PATH is unset
DYLD_LIBRARY_PATH is unset

Describe the problem

I ran into some incompatibility issues running tf.estimator.DNNClassifier with tf.contrib.learn.InputFnOps and tf.contrib.learn.Experiment.
There has been a separate solved issue:
I have tried the bundle version, but it doesn't work for me:
tensorflow==1.3
tensorflow_transform==0.3.1
six==1.10.0
However, if I switch to tf.contrib.learn.DNNClassifier, the issue go away.
It is also suggested to use tf.estimator.DNNClassifier rather than tf.contrib.learn.DNNClassifier. I would like to get tf.estimator.DNNClassifier work.

Source code / logs

def build_estimator(config):
    m = tf.estimator.DNNClassifier(
   # m = tf.contrib.learn.DNNClassifier(
    					config=config,
    					feature_columns=deep_columns,
                        hidden_units=[100, 100, 100],
                        n_classes=2,
                        optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.01)
                        )
    return m

def json_serving_input_fn():
	"""Build the serving inputs."""
	inputs = {}
	for feat in INPUT_COLUMNS:
		inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)

	features = {
	  key: tf.expand_dims(tensor, -1)
	  for key, tensor in inputs.iteritems()
	}
	return tf.contrib.learn.InputFnOps(features, None, inputs)

def _experiment_fn(run_config, hparams):
    # num_epochs can control duration if train_steps isn't
    # passed to Experiment
    train_input = lambda: model.generate_input_fn(
        hparams.train_files,
        num_epochs=hparams.num_epochs,
        batch_size=hparams.train_batch_size,
    )
    # Don't shuffle evaluation data
    eval_input = lambda: model.generate_input_fn(
        hparams.eval_files,
        batch_size=hparams.eval_batch_size,
        shuffle=False
    )
    return tf.contrib.learn.Experiment(
        model.build_estimator(
            config=run_config
        )

Error message:
local/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.py", line 440, in export_savedmodel
serving_input_receiver.receiver_tensors,
AttributeError: 'InputFnOps' object has no attribute 'receiver_tensors'

[tatianashp]

Please try using TensorFlow 1.5 and let us know if you still see an error.

[tensorflowbutler]

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

[dwujellyfish]

Found the updated code for census that works with tf 1.4