Added list of SUPPORTED_MODELS to README (#132)

Author: jkbradley

README.md

@@ -156,6 +156,13 @@ evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
 print("Training set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
 ```
 
+`DeepImageFeaturizer` supports the following models from Keras:
+
+* InceptionV3
+* Xception
+* ResNet50
+* VGG16
+* VGG19
 
 ### Distributed hyperparameter tuning
 
@@ -238,6 +245,8 @@ predictor = DeepImagePredictor(inputCol="image", outputCol="predicted_labels", m
 predictions_df = predictor.transform(image_df)
 ```
 
+`DeepImagePredictor` supports the same set of models from Keras as `DeepImageFeaturizer`.  (See above.)
+
 ##### For TensorFlow users
 Deep Learning Pipelines provides an MLlib Transformer that will apply the given TensorFlow Graph to a DataFrame containing a column of images (e.g. loaded using the utilities described in the previous section). Here is a very simple example of how a TensorFlow Graph can be used with the Transformer. In practice, the TensorFlow Graph will likely be restored from files before calling `TFImageTransformer`.
 

python/sparkdl/transformers/named_image.py

@@ -31,6 +31,9 @@
 from sparkdl.param import keyword_only, HasInputCol, HasOutputCol, SparkDLTypeConverters
 from sparkdl.transformers.tf_image import TFImageTransformer
 
+
+# If this list of supported models is expanded, update the list in the README
+# section for DeepImageFeaturizer.
 SUPPORTED_MODELS = ["InceptionV3", "Xception", "ResNet50", "VGG16", "VGG19"]