This document provides an introduction and overview of TensorFlow and how to use it with Jupyter notebooks on Google Cloud Platform (GCP). It explains that TensorFlow is Google's open source library for machine learning and was launched in 2015. It is used for many production machine learning projects. Jupyter is introduced as an interactive web-based platform for data analysis that can also be used as a TensorFlow runtime environment. The document then provides details on the programming paradigm and model of TensorFlow, giving an example of using it for a least squares method problem to predict temperatures. It explains the key components of defining a model, loss function, and training algorithm to optimize variables in a session.

1. Google confidential | Do not distribute
Your first TensorFlow programming
with Jupyter
Etsuji Nakai
Cloud Solutions Architect at Google
2016/08/29 ver1.1

2. Etsuji Nakai
Cloud Solutions Architect at Google
The author of “Introduction to Machine Learning
Theory” (Japanese Book)
New book “ML programming with TensorFlow”
will be published soon!
$ who am i

3. Google's open source library for
machine intelligence
tensorflow.org launched in Nov 2015
Used by many production ML projects
What is TensorFlow?

4. Web based interactive data analysis
platform.
Can be used as a TensorFlow
runtime environment.
What is Jupyter?
How to use Jupyter on GCP? (Japanese Blog)
http://enakai00.hatenablog.com/entry/2016/07/03/201117

5. ● All calculations are done in a “Session”
● The session contains:
○ Placeholders : where you put actual data
○ Variables : to be optimized by the algorithm
○ Functions : consisting of placeholders and variables
○ Training algorithm : to optimize the variables
Programming Paradigm of TensorFlow

6. Programming Paradigm of TensorFlow
● Three steps to write a program with TnesorFlow
○ Define a model with placeholders, variables, functions.
○ Define a loss function and a training algorithm.
○ Run session to optimize the variables minimizing the loss
function.

7. Example: Least Squares Method
● Figure out a smooth curve which predicts
next year’s temperature.
● In matrix representation:
Monthly average temperature in Tokyo.
VariablePlaceholder
Function

8. ● Define a loss function
● In matrix representation:
Example: Least Squares Method
Placeholder
Function
Observed temperature
Prediction vs Observed Values

9. ● The matrix representations can be directly
translated into TensorFlow codes.
Example: Least Squares Method
x = tf.placeholder(tf.float32, [None, 5])
w = tf.Variable(tf.zeros([5, 1]))
y = tf.matmul(x, w)
t = tf.placeholder(tf.float32, [None, 1])
loss = tf.reduce_sum(tf.square(y-t))

10. ● Specify an optimization algorithm.
● Finally, prepare a session and run the optimization loop.
Example: Least Squares Method
sess = tf.Session()
sess.run(tf.initialize_all_variables())
i = 0
for _ in range(100000):
i += 1
sess.run(train_step, feed_dict={x:train_x, t:train_t})
if i % 10000 == 0:
loss_val = sess.run(loss, feed_dict={x:train_x, t:train_t})
print ('Step: %d, Loss: %f' % (i, loss_val))
train_step = tf.train.AdamOptimizer().minimize(loss)
Putting actual data values in placeholders

11. ● You can see the actual result at:
○ http://goo.gl/Dojgp4
Example: Least Squares Method

12. Demo: More Interesting Examples!

13. Thank you!