bil 722: advanced topics in computer vision · bil 722: advanced topics in computer vision...

BIL 722: Advanced

Topics in Computer

Vision

TensorFlow Tutorial

Mehmet Kerim Yücel

Brunel University London

What is TensorFlow?

Open Source Software Library for Machine Learning-

Google- ’s second generation API for ML after DistBelief

Source code released on - 9/11/2015

Used in various Google Products-

DeepDream, RankBrain, Youtube, Translate,etc...-

Developed by Caffe & Theano- ’s lead contributors and

Google Brain team

29 February 2016

TensorFlow Tutorial 2


What is TensorFlow?

- Developed in C++ / Python

- Usage: C++ / Python

- Python preferred

- PEP-8 Convention & Google Python Style Guide

- MacOS/Linux , no support for Windows

- Docker install for Windows

- A good lead would be here

https://caffinc.github.io/2015/11/tensorflow-windows/

29 February 2016

TensorFlow Tutorial 3

https://caffinc.github.io/2015/11/tensorflow-windows/


Where to begin?29 February 2016

4

- GitHub

https://github.com/tensorflow/tensorflow

- Website

https://www.tensorflow.org/

- Installation

https://www.tensorflow.org/versions/r0.7/get_started/os_setup.html#download-and-setup

- White Paper

http://download.tensorflow.org/paper/whitepaper2015.pdf

TensorFlow Tutorial

https://github.com/tensorflow/tensorflow

https://www.tensorflow.org/

https://www.tensorflow.org/versions/r0.7/get_started/os_setup.html

http://download.tensorflow.org/paper/whitepaper2015.pdf


TensorFlow Input Formats29 February 2016

5

Protocol buffers

- Binary Format

- Text Format

An extensive guide on how to “read” data

- Feeding mechanism

- Reading from files

- Preloaded data

https://www.tensorflow.org/versions/r0.7/how_tos/reading_data/index.html#reading-data

TensorFlow Tutorial

https://www.tensorflow.org/versions/r0.7/how_tos/reading_data/index.html


TensorFlow Building Blocks29 February 2016

6

Two major constituents

- Computation Graph (Construction Phase)

- Graph Execution (Execution Phase)

TensorFlow Tutorial



7

Computation Graph (Construction Phase)

Graph nodes: operations (source ops & other ops)-

Tensors: representing data (variables, constants)-

Functions: conv- 2d, relu, max_pool, matmul, etc...

Feeding Mechanism-

Using placeholders to symbolically declare tensors-

Feed them during execution-

TensorFlow Tutorial



8

Graph Execution (Execution Phase)

- Sessions

- Launch constructed graph (default session, interactive session, etc...)

- Ability to choose devices for certain executions

- Subgraph Execution

- Session parallelization

- Multiple sessions per graph, multiple graphs per session (?)

TensorFlow Tutorial


TensorFlow – CNN 29 February 2016

9

A simple example

Slightly modified version of official TensorFlow tutorial-

https://www.tensorflow.org/versions/r0.7/tutorials/mnist/pros/index.html

4 - layer CNN with various components

Dropout, max pooling, ReLU, convolution-

Adam Optimizer minimizing cross entropy-

TensorFlow Tutorial



TensorFlow – CNN (Construction)29 February 2016

10TensorFlow Tutorial

Placeholders for future use

Data reshaping

Convolution and max pooling

wrappers

Variables will be initialized later on


TensorFlow – CNN (Construction)29 February 2016


1.Convolutional Layer

2.Convolutional Layer

3.Densely Connected Layer

(with Dropout and Softmax)

4. Cross Entropy minimization

via Adam Optimizer


TensorFlow – CNN (Execution)29 February 2016


Session and variable 1.

initialization

Accuracy definition2.

3. Train the network

4. Test the network


TensorFlow - Visualization29 February 2016


TensorBoard – Visualization Tool

- Arguably the best monitoring/debugging tool for ML/DL

tools

- Utilises summaries and name scopes




Modify the presented model

- Train 1000 times, save values in every 100th iteration

- Namescopes define layers

- Scalar & histogram summaries keep track of values




One can change the layer as

they wish

Here, we divide the network

into layers for modular tracking

of weights/biases/loss values

and accuracy




Merge summaries

Write the summary of the graph

into given directory

In every 100th iteration, get all

summary values & log them

Launch TensorBoard


TensorFlow –Pros and Cons29 February 2016

21

- No 3d conv (yet)

- Speed/memory problems

- CNN benchmarks for popular imagenet models

https://github.com/soumith/convnet-benchmarks

- Not many models around

- Workaround: convert Caffe models to TensorFlow

https://github.com/ethereon/caffe-tensorflow

- Subgraph execution

TensorFlow Tutorial

https://github.com/soumith/convnet-benchmarks

https://github.com/ethereon/caffe-tensorflow


TensorFlow –Pros and Cons29 February 2016

22

- TensorBoard (debug + visualization)

- Maintained by (quite possibly) the best devs around

- Distributed TensorFlow on its way!

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/distributed_runtime/REA

DME.md

- A (fairly) good review of related libraries

https://github.com/zer0n/deepframeworks

https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

TensorFlow Tutorial

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/distributed_runtime/README.md

https://github.com/zer0n/deepframeworks

https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software


TensorFlow –References29 February 2016

23

- Official documentation link best source

- Presented code adopted from

https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/mnist_w

ith_summaries.py


TensorFlow Tutorial

https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py



Q & A

29 February 2016


Thank you for listening

bil 722: advanced topics in computer vision · bil 722: advanced topics in computer vision...

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