Tensor Flow from GoogleA closer look

– Nikhil Krishna

Hi! My name is Nikhil and I am not a data scientist or a

computer scientist or any kind of scientist!

Agenda

What is Tensor Flow?Tensor Flow computational modelTensor Flow capabilitiesWhat is not supportedVisualizing your model with Tensor Board

Building a simple image classifier with Tensor FlowInstallation and setupRunning the Image Classifier example

Tensor Flow Performance and Parallelism

What is Tensor Flow?

Tensor Flow is a powerful library for doing large-scale numerical computation using data flow graphs

Nodes in the graph represent mathematical operations while the edges represent multidimensional data arrays (tensors)

Built by the Google Brain team for ML and deep neural networks research

The computational model used in Tensor Flow

Computations are represented as graphs

The nodes are called ops (operations) and can take 0…n tensors as input and output 0…n tensors

A tensor is a typed multi-dimensional array

The tensor flow graph is a description of computations.

In order to compute anything the graph is launched in a session which places the graph on devices

Tensor Flow Capabilities

It’s written in C/C++ but has strong python support. It integrates well with iPython so it’s easy to use interactively

Tensor Flow can run CPU or GPU and on Desktop, Server and Mobile (Android, iOS and even Raspberry Pi)

Flexibly assign compute elements of your graph to devices (CPU, GPU) and let Tensor Flow handle the distribution of the copies

Easily setup a distributed cluster and distribute your graph across it.

Installing Tensor Flow

Multiple ways to do it – Docker, Anaconda, PIP and source code.

Anaconda seems to be a consistent way to do it that has the added advantage of bundling other data manipulation and machine learning packages like SciKit Learn, Numpy, etc

If you machine has NVidia GPU then you can leverage Tensor Flow on GPU by installing the CUDA toolkit.

Recommend installing iPython as well. It’s a great way to explore the Tensor Flow Library.

Let’s look at iPython

What’s not supported?

No Windows support :( - This is because Tensor Flow uses the Bazel build system that does not support Windows. You can try with Docker images - YMMV

Python and C++ API’s - the Python one being the primary API

Creating a Tensor Flow Cluster has a lot of manual steps at this point

Visualising your model with Tensor Board

The tensor board is a visualization tool that can be used to visualize your tensor flow graph, plot metrics of the graph execution and show additional data like images flowing through the graph

The tensor board can be run either when the tensor flow graph is being executed or after completion

The tensor board picks up the log data that has been generated by the summary writer module when executing the tensor flow graph

Building an image classifier

How are we going to do image classification?

The process of categorising a group of images while only using some basic features that describe them.

Logistic regression, Support Vector Machines, Naive Bayes and Neural Networks are common classification algorithms

We are going to use the Inception Convolutional Neural Network from Google in our image classifier

Convolutional Neural Networks

At its most basic, convolutional neural networks can be thought of as a kind of neural network that uses many identical copies of the same neuron.

Like in programming when we reuse code, CNN learns a neuron and use it in many places making it easier to learn large models with smaller error.

Inception V3 Model

This is a CNN model that has been trained by Google on 1000 categories supplied by the ImageNet competition to near human accuracy.

We will retrain the model (transfer learning) to help us classify arbitrary image classifications

We are going to retrain the final layer of the classification. This is possible because the CNN uses multiple layers to fine tune classification.

Re-training Inception

Download the creative commons images of flowers and create a directory structure with class names as sub-directories.

Run the retraining script. We can tweak the parameters to reduce the time taken or increase the accuracy of the classifier

This script loads the pre-trained Inception v3 model, removes the old final layer, and trains a new one on the flower photos.

Let’s look at some code

Distributed Tensor Flow

A Tensor Flow ‘cluster’ is a set of ‘tasks’ that participate in the distributed execution of a Tensor Flow graph.

Each task is associated to a Tensor Flow ‘server’ which contains a ‘master’ that can be used to create sessions and a ‘worker’ that executes operations in the graph.

Each task typically runs on a separate machine but you can run multiple tasks on the same machine.

Questions

The training accuracy shows the percentage of the images used in the current training batch that were labeled with the correct class.

Validation accuracy: The validation accuracy is the precision (percentage of correctly-labelled images) on a randomly-selected group of images from a different set.

Cross entropy is a loss function that gives a glimpse into how well the learning process is progressing. (Lower numbers are better here.)

Bottleneck' is an informal term for the layer just before the final output layer that actually does the classification. This penultimate layer has been trained to output a set of values that's good enough for the classifier to use to distinguish between all the classes it's been asked to recognize.

Because every image is reused multiple times during training and calculating each bottleneck takes a significant amount of time, it speeds things up to cache these bottleneck values on disk so they don't have to be repeatedly recalculated. By default they're stored in the /tmp/bottleneck directory, and if you rerun the script they'll be reused so you don't have to wait for this part again.

So whats a classifier?

A classifier is a function that takes some data as input and assigns a label to it as output

Supervised learning lets you write a classifier automatically

Getting good data and identifying features

It’s all about the dataThere are certain publicly available datasets that are used for learning

TF Learn module has an API to download MNIST, IRIS, Boston Housing datasets

Very useful for learning and understanding the concepts and quickly bootstrap yourself.

We are going to look at MNIST and the IRIS datasets