The TensorFlow dancecraze

Gabe Hamilton

TensorFlow is a Machine Learning library

What is Machine Learning?

usually: Statistical Pattern Matching

Using past measurements to predict something about a new piece of information

Does this picture contain a cat? Classification

How much is this home worth? Regression

Regression Talk https://docs.google.com/presentation/d/17FoojZ17DKDZqoByhB16PmtAXtnJtXSy1-feK9-A_fg/edit#slide=id.p10

There are lots of other ML libraries

Is it right for you?

Maybe Not, use Prediction APIhttp://www.slideshare.net/gabehamilton/intro-to-google-prediction-api-15167420

Or equivalent Amazon or Microsoft APIs

if you have a CSV of observed data.

But I’d like to get into the details or I am working on

a complex problem...

You do still have some choices.Some people prefer Keras on top of TensorFlow https://keras.io/ or tf-slim or pretty-tensor

So why use TensorFlow?

It’s not much different than the “easier” libraries.

You’ll want to understand more and more details anyway.

Because you like

It has the hottest dance moves!

Look out a Tensor!

Some Math and a Multi-Dimensional Array had a baby

For our purposesIt’s a multi-dimensional array.

Just remember that you are putting a mathematical construct in that array.

A tensor is a multi-dimensional array with certain transformation properties

You have some dataEven though it’s in a 2D spreadsheet it can describe a multidimensional space.

House Price Data 1 mile to park 2 bedrooms 1800 sq ft

So our Tensor is [1, 2, 1800]Square footage

Distance to park

# of bathrooms

Let’s build a Tensor Machine

= a Tensor

Comparing outputis our Output

But we are expecting

- = Loss

Iterate until we have a good model

DemoSimple Regression

https://github.com/gabehamilton/code-from-talks/tree/master/tensorflow_intro

Demo (run in Datalab)

Demo code: fitting a line to pointsDemo is of Datalab notebook# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3

tf.reset_default_graph()

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

x_measured = np.random.rand(100).astype(np.float32)

y_measured = x_measured * (0.1 + (0.05 *np.random.rand(100))) + 0.3

plt.scatter(x_measured, y_measured)

plt.xlabel('<- Bluer : Uniform Color : Redder ->')

plt.ylabel('Deaths per mission')

plt.title('Star Trek Uniform Color Mortality Incidence')

# plt.plot([0, 1], [0, 0.5], color='purple', lw=2)

# plt.plot([0, 1], [0.25, 0.5], color='purple', lw=2)

Measured Data

Demo code continuedimport tensorflow as tf

# Try to find values for slope and offset that match

# y_measured = slope * x_measured + offset

slope = tf.Variable(tf.random_uniform([1], -1.0, 1.0))

offset = tf.Variable(tf.zeros([1]))

y_predicted = slope * x_measured + offset

loss = tf.reduce_mean(tf.square(y_predicted - y_measured))

optimizer = tf.train.GradientDescentOptimizer(0.5)

train = optimizer.minimize(loss)

A test value for slope

Demo code continued# Launch the graph.

sess = tf.Session()

sess.run(tf.initialize_all_variables()) # and initialize variables

# Fit the line.

print 'step #\t', 'slope\t\t', 'offset'

for step in range(201):

sess.run(train)

if step % 20 == 0:

print step,'\t', sess.run(slope)[0], '\t', sess.run(offset)[0]

A test value for slope and offset

Q & APlus more slides for longer talks or

answering questions

Why TensorFlow is useful

Build your graph in a high level language, execute in fast implementations.

Distribute graph operations across processors.

Whole graph can be optimized.

4+ D Spaces

A given measurement all fits into a simple Tensor: [4, 4, 4, 3]

Some Tensors

Scalar 0.2Vector [1, 2, 1800]Matrix [2, 1, 1], [1, 2, 0], [0, 1, 0]

n-Tensors

Rank 2 Tensor

When a bunch of vectors hang out they make a Matrix

[2, 1, 1],[1, 2, 0],[0, 1, 0]