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The TensorFlow dance
craze
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/17FoojZ17DKDZqoByhB16PmtAXtnJ
tXSy1-feK9-A_fg/edit#slide=id.p10
There are lots of other ML libraries
Is it right for you?
Maybe Not, use Prediction API
http://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 purposes
It’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 data
Even 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 output
is our Output
But we are expecting
- = Loss
Iterate until we have a good model
Demo
Simple Regression
https://github.com/gabehamilton/code-from-talks/tree/master/tensorflow_intro
Demo
(run in Datalab)
Demo code: fitting a line to points
Demo 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 continued
import 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', 'slopett', '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 & A
Plus 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 0.2
Vector [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]

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The TensorFlow dance craze

  • 2. TensorFlow is a Machine Learning library
  • 3. What is Machine Learning? usually: Statistical Pattern Matching Using past measurements to predict something about a new piece of information
  • 4. Does this picture contain a cat? Classification How much is this home worth? Regression Regression Talk https://docs.google.com/presentation/d/17FoojZ17DKDZqoByhB16PmtAXtnJ tXSy1-feK9-A_fg/edit#slide=id.p10
  • 5. There are lots of other ML libraries
  • 6. Is it right for you? Maybe Not, use Prediction API http://www.slideshare.net/gabehamilton/intro-to-google-prediction-api-15167420 Or equivalent Amazon or Microsoft APIs if you have a CSV of observed data.
  • 7. But I’d like to get into the details or I am working on a complex problem...
  • 8. You do still have some choices. Some people prefer Keras on top of TensorFlow https://keras.io/ or tf-slim or pretty-tensor
  • 9. 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!
  • 10. Look out a Tensor!
  • 11. Some Math and a Multi-Dimensional Array had a baby
  • 12. For our purposes It’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
  • 13. You have some data Even 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
  • 14. Let’s build a Tensor Machine = a Tensor
  • 15. Comparing output is our Output But we are expecting - = Loss
  • 16. Iterate until we have a good model
  • 19. Demo code: fitting a line to points Demo 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
  • 20. Demo code continued import 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
  • 21. Demo code continued # Launch the graph. sess = tf.Session() sess.run(tf.initialize_all_variables()) # and initialize variables # Fit the line. print 'step #t', 'slopett', '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
  • 22. Q & A Plus more slides for longer talks or answering questions
  • 23. 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.
  • 24. 4+ D Spaces A given measurement all fits into a simple Tensor: [4, 4, 4, 3]
  • 25. Some Tensors Scalar 0.2 Vector [1, 2, 1800] Matrix [2, 1, 1], [1, 2, 0], [0, 1, 0] n-Tensors
  • 26. Rank 2 Tensor When a bunch of vectors hang out they make a Matrix [2, 1, 1], [1, 2, 0], [0, 1, 0]