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I am using tf.contrib.learn.DNNLinearCombinedRegressor with wide and deep feature columns. I use an input_fn to send data to this regressor for training. I have both real and categorical features, and so my deep feature columns are made of sparse columns with embedding as well as real valued columns. While my linear columns are real valued columns. After training, I can naively access the trained model for prediction by using the estimator.predict_scores() method. However, to my understanding, this recreates the tensorflow graph from the checkpoint file, which is slow for my intended application. I then tried to use estimator.export_savedmodel(), to see if I can save and load the graph prior to actual prediction in my application. But I got stuck with it. I am facing the following issues:

  1. To export, I did the following:

    feature_spec = tf.feature_column.make_parse_example_spec(deep_feature_cols)

export_input_fn = tf.contrib.learn.utils.build_parsing_serving_input_fn(feature_spec) 

servable_model_path = regressor.export_savedmodel(export_dir_base = servable_model_dir, serving_input_fn = export_input_fn, as_text= True)

(note that the raw and default parsing functions didn't work for me due to VarLen feature column -- the embedding column). Since I construct feature_spec with my deep columns only, I don't understand how the saved model will know what I am using for my linear columns. I don't know how to combine the two types of columns for saving/exporting.

  1. While I was able to export the trained regressor model successfully, I couldn't figure out how to use it for predictions. I have seen tutorials for doing this using Tensorflow Serving such as this. However, I just want something simple that I load and use in python. I am working on an interactive GUI application in Windows and so don't want to use bazel to build a saved model for Tensorflow Serving. I tried the following from this post, but it didn't work with my input_fn

    from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model(servable_model_path)
predictions = predict_fn(get_input_fn(X, y, LABEL, num_epochs=1, shuffle=False))

where get_input_fn is my custom input_fn. When I run this code, I get the following error: 

AttributeError      Traceback (most recent call last)
<ipython-input-15-128f4d1ba155> in <module>()
----> 1 predictions = predict_fn(get_input_fn(X, y, LABEL, num_epochs=1, 
shuffle=False))

~\AppData\Local\Continuum\Anaconda3\envs\tensorflow\lib\site-
packages\tensorflow\contrib\predictor\predictor.py in __call__(self, input_dict)
 63     """
 64     # TODO(jamieas): make validation optional?
---> 65     input_keys = set(input_dict.keys())
 66     expected_keys = set(self.feed_tensors.keys())
 67     unexpected_keys = input_keys - expected_keys

AttributeError: 'function' object has no attribute 'keys'

Any input is appreciated. I am on windows 10, using Tensorflow 1.3 with Python 3.5 in Anaconda virtual environment.

Ruta Desai
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  • I am running into the same error - did you make progress at all? Any pointers would be much appreciated. – jjreddick Nov 11 '17 at 07:29

1 Answers1

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Poked around a bit more and the predict_fn expects a dictionary with the key set to the exact same name as the feature variable your model expects. In my CNN model the input was "inputs" as below:

def _cnn_model_fn(features, labels, mode):
  # Input Layer
  print("features shape", features['inputs'])

Here's what I passed in the predict_fn where myimage is a flattened np.array.

predictions = predict_fn({"inputs": [myimage]})
jjreddick
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