minor edits

Author: Lee Yang

examples/mnist/estimator/mnist_spark.py

@@ -93,7 +93,6 @@ def model_fn(features, labels, mode):
         train_op=optimizer.minimize(
             loss, tf.compat.v1.train.get_or_create_global_step()))
 
-  # strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
   config = tf.estimator.RunConfig(train_distribute=strategy, save_checkpoints_steps=100)
 
   classifier = tf.estimator.Estimator(

examples/mnist/keras/README.md

@@ -130,7 +130,7 @@ For batch inferencing use cases, you can use Spark to run multiple single-node T
     ${TFoS_HOME}/examples/mnist/keras/mnist_inference.py \
     --cluster_size ${SPARK_WORKER_INSTANCES} \
     --images_labels ${TFoS_HOME}/data/mnist/tfr/test \
-    --export_dir $SAVED_MODEL \
+    --export_dir ${SAVED_MODEL} \
     --output ${TFoS_HOME}/predictions
 
 #### Train and Inference via Spark ML Pipeline API