official/mnist: support savedmodel (#2967)

With examples, and updates to the README

official/mnist/README.md

@@ -20,3 +20,36 @@ python mnist.py
 
 The model will begin training and will automatically evaluate itself on the
 validation data.
+
+## Exporting the model
+
+You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format by using the argument `--export_dir`:
+
+```
+python mnist.py --export_dir /tmp/mnist_saved_model 
+```
+
+The SavedModel will be saved in a timestamped directory under `/tmp/mnist_saved_model/` (e.g. `/tmp/mnist_saved_model/1513630966/`).
+
+**Getting predictions with SavedModel**
+Use [`saved_model_cli`](https://www.tensorflow.org/programmers_guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
+
+```
+saved_model_cli run --dir /tmp/mnist_saved_model/TIMESTAMP --tag_set serve --signature_def classify --inputs image_raw=examples.npy
+```
+
+`examples.npy` contains the data from `example5.png` and `example3.png` in a numpy array, in that order. The array values are normalized to values between 0 and 1.
+
+The output should look similar to below:
+```
+Result for output key classes:
+[5 3]
+Result for output key probabilities:
+[[  1.53558474e-07   1.95694142e-13   1.31193523e-09   5.47467265e-03
+    5.85711526e-22   9.94520664e-01   3.48423509e-06   2.65365645e-17
+    9.78631419e-07   3.15522470e-08]
+ [  1.22413359e-04   5.87615965e-08   1.72251271e-06   9.39960718e-01
+    3.30306928e-11   2.87386645e-02   2.82353517e-02   8.21146413e-18
+    2.52568233e-03   4.15460236e-04]]
+```
+

official/mnist/mnist.py

@@ -58,6 +58,10 @@ parser.add_argument(
     'with CPU. If left unspecified, the data format will be chosen '
     'automatically based on whether TensorFlow was built for CPU or GPU.')
 
+parser.add_argument(
+    '--export_dir',
+    type=str,
+    help='The directory where the exported SavedModel will be stored.')
 
 def train_dataset(data_dir):
   """Returns a tf.data.Dataset yielding (image, label) pairs for training."""
@@ -152,6 +156,9 @@ def mnist_model(inputs, mode, data_format):
 
 def mnist_model_fn(features, labels, mode, params):
   """Model function for MNIST."""
+  if mode == tf.estimator.ModeKeys.PREDICT and isinstance(features,dict):
+    features = features['image_raw']
+  
   logits = mnist_model(features, mode, params['data_format'])
 
   predictions = {
@@ -160,7 +167,9 @@ def mnist_model_fn(features, labels, mode, params):
   }
 
   if mode == tf.estimator.ModeKeys.PREDICT:
-    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
+    export_outputs={'classify': tf.estimator.export.PredictOutput(predictions)}
+    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions,
+                                      export_outputs=export_outputs)
 
   loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
 
@@ -222,6 +231,13 @@ def main(unused_argv):
   print()
   print('Evaluation results:\n\t%s' % eval_results)
 
+  # Export the model
+  if FLAGS.export_dir is not None:
+    image = tf.placeholder(tf.float32,[None, 28, 28])
+    serving_input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(
+        {"image_raw":image})
+    mnist_classifier.export_savedmodel(FLAGS.export_dir, serving_input_fn)
+
 
 if __name__ == '__main__':
   tf.logging.set_verbosity(tf.logging.INFO)