Addressed PR comments

Author: FarhanTejani

sagemaker-python-sdk/tensorflow_keras_cifar10/cifar10_cnn.py

@@ -1,3 +1,16 @@
+#     Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+#     Licensed under the Apache License, Version 2.0 (the "License").
+#     You may not use this file except in compliance with the License.
+#     A copy of the License is located at
+#    
+#         https://aws.amazon.com/apache-2-0/
+#    
+#     or in the "license" file accompanying this file. This file is distributed
+#     on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+#     express or implied. See the License for the specific language governing
+#     permissions and limitations under the License.
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -66,7 +79,18 @@ def keras_model_fn(hyperparameters):
     return _model
 
 
-def serving_input_fn(params):
+def serving_input_fn(hyperparameters):
+    """This function defines the placeholders that will be added to the model during serving.
+    The function returns a tf.estimator.export.ServingInputReceiver object, which packages the 
+    placeholders and the resulting feature Tensors together.
+    For more information: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/README.rst#creating-a-serving_input_fn
+    
+    Args:
+        hyperparameters: The hyperparameters passed to SageMaker TrainingJob that runs your TensorFlow 
+                        training script.
+    Returns: ServingInputReceiver or fn that returns a ServingInputReceiver
+    """
+    
     # Notice that the input placeholder has the same input shape as the Keras model input
     tensor = tf.placeholder(tf.float32, shape=[None, HEIGHT, WIDTH, DEPTH])
 
@@ -75,12 +99,14 @@ def serving_input_fn(params):
     return tf.estimator.export.ServingInputReceiver(inputs, inputs)
 
 
-def train_input_fn(training_dir, params):
+def train_input_fn(training_dir, hyperparameters):
+    """Returns input function that would feed the model during training"""
     return _input(tf.estimator.ModeKeys.TRAIN,
                     batch_size=BATCH_SIZE, data_dir=training_dir)
 
 
-def eval_input_fn(training_dir, params):
+def eval_input_fn(training_dir, hyperparameters):
+    """Returns input function that would feed the model during evaluation"""
     return _input(tf.estimator.ModeKeys.EVAL,
                     batch_size=BATCH_SIZE, data_dir=training_dir)
 
@@ -120,6 +146,7 @@ def _input(mode, batch_size, data_dir):
     iterator = dataset.batch(batch_size).make_one_shot_iterator()
     images, labels = iterator.get_next()
 
+    # We must use the default input tensor name PREDICT_INPUTS
     return {PREDICT_INPUTS: images}, labels
 
 

sagemaker-python-sdk/tensorflow_keras_cifar10/tensorflow_keras_CIFAR10.ipynb

@@ -164,7 +164,7 @@
    "source": [
     "This function builds and returns a compiled Keras model.\n",
     "\n",
-    "**Note:** The first layer is named `PREDICT_INPUTS`. This serves as a workaround for a known issue where SageMaker does not recognize the default (or any custom) name for the first layer of Keras models. Furthermore, note that we are wrapping our model in a `tf.keras.Model` before returning it. This serves as a workaround for a known issue where a Sequential model cannot be directly converted into an estimator. See [here](https://github.com/tensorflow/tensorflow/issues/20552) for more information about the issue.\n",
+    "**Note:** The first layer is named `PREDICT_INPUTS`. This serves as a workaround for a known issue where TensorFlow does not recognize the default (or any custom) name for the first layer of Keras models. Furthermore, note that we are wrapping our model in a `tf.keras.Model` before returning it. This serves as a workaround for a known issue where a Sequential model cannot be directly converted into an Estimator. See [here](https://github.com/tensorflow/tensorflow/issues/20552) for more information about the issue.\n",
     "\n",
     "### Input functions\n",
     "These functions are similar to those required by any other model using the TensorFlow Estimator API."
@@ -285,21 +285,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "conda_tensorflow_p27",
+   "display_name": "conda_tensorflow_p36",
    "language": "python",
-   "name": "conda_tensorflow_p27"
+   "name": "conda_tensorflow_p36"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   },
   "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
  },