Adding detail to abalone-node README (#861)

* Updating TensorFlow directions to accurately note the supported versions of Node.js

* updating dependencies to the latest versions

* Updating code based on changes to how some packages work since previous Node.js package update

* remove commended code

* updating README to clarify some steps

* updating README to add detail

* updates to addition-rnn README to explain how to run the demo

Author: Jen Person

abalone-node/README.md

@@ -1,23 +1,33 @@
 # TensorFlow.js Example: Abalone Age
 
-This example shows how to predicting the age of abalone from physical measurements under Node.js
+This example shows how to predict the age of abalone from physical measurements using TensorFlow.js with Node.js.
 
 The data set available at [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Abalone).
 
-This example shows how to
-* load a `Dataset` from a local csv file
-* prepare the Dataset for training
-* create a `tf.LayersModel` from scratch
-* train the model through `model.fitDataset()`
+This example shows how to:
+* load a `Dataset` from a local csv file.
+* prepare the Dataset for training.
+* create a `tf.LayersModel` from scratch.
+* train the model through `model.fitDataset()`.
 * save the trained model to a local folder.
 
-To launch the demo, do
+To launch the demo, run the following command:
 
 ```sh
 yarn
 yarn train
 ```
 
+The result logs 100 Epochs as well as a predicted result similar to the following:
+
+```
+...
+Epoch 100 / 100
+eta=0.0 =================================================>
+402ms 57414us/step - loss=7.42 val_loss=5.60
+The actual test abalone age is 10, the inference result from the model is 11.929240226745605
+```
+
 By default, the training uses tfjs-node, which runs on the CPU.
 If you have a CUDA-enabled GPU and have the CUDA and CuDNN libraries
 set up properly on your system, you can run the training on the GPU

addition-rnn/README.md

@@ -9,3 +9,16 @@ Note: this example is a port of the Keras addition RNN example with a UI.
 You can find the original Keras python code [here](https://github.com/keras-team/keras/blob/master/examples/addition_rnn.py).
 
 [See this example live!](https://storage.googleapis.com/tfjs-examples/addition-rnn/dist/index.html)
+
+To run this demo locally on your machine, run the following commands:
+
+```sh
+yarn
+yarn watch
+```
+
+The demo runs on port http://localhost:1234.
+
+Once the page loads, click **Train model** to train. You can view the model's progress through graphs displayed on the page.
+
+Upon completion of training, view the model's success through displayed test examples.