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?

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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

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● 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

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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.

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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

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

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● 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))

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● 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

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

12.
Demo: More Interesting Examples!

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Thank you!