Updates to individual official/ README files to make sure users can run the models

official/mnist/README.md

@@ -11,9 +11,8 @@ APIs.
 
 ## Setup
 
-To begin, you'll simply need the latest version of TensorFlow installed,
-and make sure to run the command to export the `/models` folder to the
-python path: https://github.com/tensorflow/models/tree/master/official#running-the-models
+To begin, you'll simply need the latest version of TensorFlow installed.
+First make sure you've [added the models folder to your Python path](official/README.md#running-the-models).
 
 Then to train the model, run the following:
 

official/resnet/README.md

@@ -18,8 +18,9 @@ Please proceed according to which dataset you would like to train/evaluate on:
 ### Setup
 
 You simply need to have the latest version of TensorFlow installed.
+First make sure you've [added the models folder to your Python path](official/README.md#running-the-models).
 
-First download and extract the CIFAR-10 data from Alex's website, specifying the location with the `--data_dir` flag. Run the following:
+Then download and extract the CIFAR-10 data from Alex's website, specifying the location with the `--data_dir` flag. Run the following:
 
 ```
 python cifar10_download_and_extract.py

official/wide_deep/README.md

@@ -15,7 +15,7 @@ The input function for the `Estimator` uses `tf.contrib.data.TextLineDataset`, w
 The `Estimator` and `Dataset` APIs are both highly encouraged for fast development and efficient training.
 
 ## Running the code
-Make sure to run the command to export the `/models` folder to the python path: https://github.com/tensorflow/models/tree/master/official#running-the-models
+First make sure you've [added the models folder to your Python path](official/README.md#running-the-models).
 
 ### Setup
 The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this sample uses for training is hosted by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). We have provided a script that downloads and cleans the necessary files.