1. Tensorflow Tutorial
Homin Yoon

2. What is Tensorflow? 01
Is a Open source software library for numerical computation using data flow graphs.
Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors)
Open source software library for numerical computation
It uses data flow graphs.(Node : operation, Edge : tensor)

3. What is Tensorflow? Data Flow Graph01
Data Flow Graph
Nodes in the graph represent mathematical operations
Edges describe the i/o relationships between nodes
Data edges carry dynamically-sized multidimensional data arrays, or tensors
Is a Open source software library for numerical computation using data flow graphs.
Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors)

4. What is Tensorflow? Machine Learning requires complex computations.01
Is a Open source software library for numerical computation using data flow graphs.
Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors)
Machine Learning requires complex computations.
They are hard to display.(Tensor board)

5. Installation Tensorflow suits with Linux02
Tensorflow suits with Linux
Virtual machine is not helpful because it is impossible to access process unit when it has compuation
Therefore, I have installed ubuntu by using dual boot

6. Installation We have to install python 2.7version02
We have to install python 2.7version
Because newest version doesn’t support all library
To import tensorflow, we should install anaconda
Otherwise, there would be import error

7. Installation If you want to use GPU for computation for ML02
If you want to use GPU for computation for ML
You have to install CUDA tool kit.
CUDA is Compute Unified Device Architecture
Just for CPU computation, cuda toolkit isn’t needed

8. Installation 02
CUDNN can boost GPU so that computation speed is increased even higher
You should give a permission of CUDNN library
To every user in Ubuntu

9. Installation Finally, Only tensorflow package is left02
Finally, Only tensorflow package is left
Download tensorflow source file and compile with Bazel Compiler made by google

10. Installation Platform: Ubuntu16.04(Linux)02
Platform: Ubuntu16.04(Linux)
Language & tool: Python2.7 & Anaconda
For GPU: CUDA Toolkit & CUDNN
Complier for tensorflow package: Bazel
Now, we can START TENSORFLOW

11. Simple code(multiply)03
import tensorflow as tf
a = tf.placeholder("float")
b = tf.placeholder("float")
y = tf.mul(a, b)
sess = tf.Session()
print sess.run(y, feed_dict={a: 3, b: 3})

12. Simple code(multiply)03
Operation Description
tf.add sum
tf.sub substraction
tf.mul multiplication
tf.div division
tf.mod module
tf.abs return the absolute value
tf.neg return negative value

13. Simple code(multiply)03
Operations & Kernels
An operation has a name and represents an abstract computation (e.g., “matrix multiply”, or “add”)
A kernel is a particular implementation of operation that can be run on a particular device (e.g., CPU or GPU).

14. Simple code(multiply)03
Operations groups Operations
Maths Add, Sub, Mul, …
Array Concat, Slice, Split, Constant, …
Matrix MatMul, MatrixInverse, MatrixDeterminant
Neuronal Network SoftMax, Sigmoid, ReLU,
Convolution2D, MaxPool
Flow control Merge, Switch, Enter, Leave, NextIteration

15. Linear Regression 04

16. Linear Regression 04
linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more variables denoted X.

17. Linear Regression y = W * x + b y = 0.1 * x + 0.3 Cost function:04
y = W * x + b
y = 0.1 * x + 0.3
Cost function:
Mean square error
It uses gradient descent

18. Linear Regression 04

19. Clustering Tensor Shape, Rank and Dimension05
Tensor Shape, Rank and Dimension
Shape Rank Dimension-Number
[] D
[D0] D
[D0, D1] D
[D0, D1, D2] D
… … …
[D0, D1, ... Dn] n n-D

20. Clustering Data Storage in TensorFlow05
Data Storage in TensorFlow
Data files ://github.com/jorditorresBCN/
LibroTensorFlow/blob/master/input_data.py
Memory:
• Constants tf.constant(…)
• Variables tf.Variable(…)
Python code : Placeholders
• feed_dict parameter

21. Clustering import tensorflow as tf a = tf.placeholder("float")05
import tensorflow as tf
a = tf.placeholder("float")
b = tf.placeholder("float")
y = tf.mul(a, b)
sess = tf.Session()
print sess.run(y, feed_dict={a: 3, b: 3})

22. Clustering K-mean Algorithm05
K-mean Algorithm
As it is a heuristic algorithm, there is no guarantee
that it will converge to the global optimum, and the
result may depend on the initial clusters. As the
algorithm is usually very fast, it is common to run it
multiple times with different starting conditions.

23. Clustering 05 vectors = tf.constant(conjunto_puntos) k = 4
centroides = tf.Variable(tf.slice(tf.random_shuffle(vectors),[0,0],[k,-1]))
expanded_vectors = tf.expand_dims(vectors, 0)
expanded_centroides = tf.expand_dims(centroides, 1)
assignments = tf.argmin(tf.reduce_sum(tf.square(tf.sub(expanded_vectors,
expanded_centroides)), 2), 0)
means = tf.concat(0, [tf.reduce_mean(tf.gather(vectors,
tf.reshape(tf.where( tf.equal(assignments, c)),[1,-1])),
reduction_indices=[1]) for c in xrange(k)])
update_centroides = tf.assign(centroides, means)
init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)
for step in xrange(100):
_, centroid_values, assignment_values = sess.run([update_centroides centroides, assignments])

24. Clustering K-mean Algorithm05
K-mean Algorithm
Initial step (step 0): determines an initial set of K centroids.
Allocation step (step 1): assigns each observation to the nearest group.
Update step (step 2): calculates the new centroids for each new group

25. Clustering K-mean Algorithm05
K-mean Algorithm
Initial step (step 0): determines an initial set of K centroids.
Allocation step (step 1): assigns each observation to the nearest group.
Update step (step 2): calculates the new centroids for each new group

26. Clustering K-mean Algorithm There are two tensors05
K-mean Algorithm
There are two tensors
Vectors(2000 number of distributed point)
Centroid(4 centroid)

27. Clustering K-mean Algorithm print centroid_values05
K-mean Algorithm
print centroid_values
[[ e e-01]
[ e e-01]
[ e e-01]
[ e e-01]]

28. Thank you.