| class IntersectionFound(Exception): | |
| def __init__(self, user_id): | |
| self.user_id = user_id | |
| def get_friends(user_id): | |
| """Returns a set of ids representing the friends of the given user.""" | |
| def update_shortest_path(shortest_paths, user_id, to_user_id, via_user_id, distance): | |
| """Update the shortest path for one user.""" | |
| if shortest_paths.has_key(user_id): |
| import input_data | |
| import tensorflow as tf | |
| # http://www.tensorflow.org/tutorials/mnist/beginners/index.html | |
| print '\033[93m' + 'Tutorial1 from:\nhttp://www.tensorflow.org/tutorials/mnist/beginners/index.html' + '\033[0m' | |
| print 'MNIST INPUT DATA' | |
| mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) | |
| 'PLACE HOLDERS' | |
| # Make a placeholder / value to input on run |
#Dowload IMDB data for LSTM example
Here is a snippet to download python code and imdb.pkl
wget http://deeplearning.net/tutorial/code/imdb.py
wget http://deeplearning.net/tutorial/code/lstm.py
wget http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl
TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:
After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.