Merge pull request aws#81 from awslabs/r-bash

Fixes for mvs

Author: mvsusp

sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers/tensorflow_abalone_age_predictor_using_layers.ipynb

@@ -95,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**sagemaker_session.upload_data** will upload the abalone dataset from your machine to a bucket named **sagemaker-{your aws account number}**, if you don't have this bucket yet, sagemaker_session will create it for you."
+    "**sagemaker_session.upload_data** will upload the abalone dataset from your machine to a bucket named **sagemaker-{region}-{your aws account number}**, if you don't have this bucket yet, sagemaker_session will create it for you."
    ]
   },
   {
@@ -136,7 +136,7 @@
     "   return EstimatorSpec(mode, predictions, loss, train_op, eval_metric_ops)\n",
     "```\n",
     "\n",
-    "The **`model_fn`** must accept three arguments:\n",
+    "The **`model_fn`** requires three arguments:\n",
     "\n",
     "*   **`features`**: A dict containing the features passed to the model via\n",
     "    **`input_fn`**.\n",
@@ -149,7 +149,7 @@
     "        mode, namely via a `train()` call.\n",
     "    *   **`EVAL`** The **`model_fn`** was invoked in\n",
     "        evaluation mode, namely via an `evaluate()` call.\n",
-    "    *   **`PREDICT`** The `model_fn` was invoked in\n",
+    "    *   **`PREDICT`** The **`model_fn`** was invoked in\n",
     "        predict mode, namely via a `predict()` call.\n",
     "\n",
     "**`model_fn`** may also accept a **`hyperparameters`** argument containing a dict of\n",
@@ -270,7 +270,7 @@
     "        inputs=input_layer, units=10, activation=tf.nn.relu)\n",
     "    ```\n",
     "\n",
-    "*   **`tf.nn.relu6`**. The following code creates a layer of `units` nodes fully\n",
+    "*   **`tf.nn.relu`**. The following code creates a layer of `units` nodes fully\n",
     "    connected to the previous layer `hidden_layer` with a ReLU activation\n",
     "    function:\n",
     "\n",
@@ -531,7 +531,7 @@
     "* deploy your script in a container with tensorflow installed\n",
     "* copy the data from the bucket to the container\n",
     "* instantiate the tf.estimator\n",
-    "* train the estimator with 10 training steps\n",
+    "* train the estimator with 100 training steps\n",
     "* save the estimator model"
    ]
   },
@@ -637,4 +637,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard/tensorflow_resnet_cifar10_with_tensorboard.ipynb

@@ -32,7 +32,6 @@
    "source": [
     "import os\n",
     "import sagemaker\n",
-    "import tensorflow\n",
     "from sagemaker import get_execution_role\n",
     "\n",
     "sagemaker_session = sagemaker.Session()\n",
@@ -83,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**sagemaker_session.upload_data** will upload the CIFAR-10 dataset from your machine to a bucket named **sagemaker-{*your aws account number*}**, if you don't have this bucket yet, sagemaker_session will create it for you."
+    "**sagemaker_session.upload_data** will upload the CIFAR-10 dataset from your machine to a bucket named **sagemaker-{region}-{*your aws account number*}**, if you don't have this bucket yet, sagemaker_session will create it for you."
    ]
   },
   {
@@ -163,6 +162,14 @@
     "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prediction using the trained model\n",
+    ""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},